JP2004012349A - Method, apparatus, and program for adaptive control measurement and search for buried substance, and recording medium where those programs are recorded - Google Patents

Abstract

<P>PROBLEM TO BE SOLVED: To provide an adaptive control measurement method for searching for buried substances in various searching substances easily and with high precision, adaptively-controlling measurement parameters to optimum values. <P>SOLUTION: A buried substance which exists stretching in a searching substance continuously is searched on the basis of data on reflected waves, by transmitting a transmission wave from the surface of the searching substance, and receiving the reflected waves of the transmitted wave. An image processor 10 forms image data having data on the concentrations of two cross sections perpendicular to the surface of the searching substance on the basis of data on the received reflected waves, removes image data which do not contain the buried substance using the knowledge that the buried substance exists stretching continuously, forms data on images of candidate points of the two cross sections which represent a candidate point of the buried substance, stores the data on the images of the candidate points in an image memory 23 in such a way as to arrange them on a three-dimensional space imaginarily, presumes the position of the buried substance adaptively-controlling position-presuming parameters using genetic algorithm on the basis of the stored data on the candidate point images, forms and outputs an image of the buried substance in the form of a three-dimensional image. <P>COPYRIGHT: (C)2004,JPO

Description

【００９５】
ここで、ｎは抽出された埋設管の数であり、ｌ_ｉはｉ番目の埋設管の長さである。＜結果２＞より抽出されたオブジェクトは全て埋設管であるので、その長さの総和（適応度ｇ）は埋設管の抽出度合いとなる。そして、＜結果１＞が満たされたとき、適応度ｇは最大値を示す。例えば、図３５（ａ）のように埋設管が抽出されていない場合に適応度ｇは低くなり、図３５（ｂ）のように正確に抽出されていれば適応度ｇは高くなる。このように、埋設管の長さの総和が大きい方が埋設管を正確に抽出できていることになり適応度ｇは高くなる。
【００９６】
本実施形態に係る選択処理（ステップＳ９２）においては公知のルーレット選択法を用いる。ルーレット選択法は、各個体の適応度から選択確率を変え、この確率に沿ってルーレットを回すように選択を行う方法であり、適応度の高い個体ほど選ばれやすい。ある個体ｉにおける適応度をｇ_ｉとし、個体数をＭとするとき、次式に示すような選択確率Ｐ_ｉで選択を行う。
【００９７】
【数１４】

【００９８】
図３６に、本実施形態で用いたルーレット選択法による選択処理の実施例を示す。ここで、例えば本実施形態のようにそれぞれ異なるパラメータを持った１０個の個体が与えられている場合、上記数１２で示した評価関数を用いてそれぞれの適応度ｇ_ｉを算出し、その結果からそれぞれの選択確率Ｐ_ｉを求める。この選択確率Ｐ_ｉを図３６（ｄ）に示すようにルーレットに配置したとき、選択確率Ｐ_ｉが大きいほど大きな占有率であるため最も選択される確率は高いということになる。
【００９９】
次いで、交叉処理（ステップＳ９３）においては、いわゆる一点交叉法を用いる。一点交叉法は、図３７（ａ）及び（ｂ）に示すように、交叉確率ｐ_ｃで個体を２つ選び、１から１１１のビット境界においてランダムに交叉点を決定し、その点を境に後ろの遺伝子列を部分的に交換する。
【０１００】
さらに、突然変異処理（ステップＳ９４）においては、図３８に示すように、ランダムに個体を選び、突然変異確率ｐ_ｍで突然変異を行うビットを１から１１２のビット中から決定し、そのビットを反転させる。
【０１０１】
図３９は、図２の埋設物探査システムの実施例における世代数Ｔに対する適合度の推移を示すグラフである。本実施形態においては、図３に示すループにより世代数（反復回数）を変化させており、最終世代（反復回数）Ｔ＝１０００になるまでパラメータを更新することが好ましい。また、とって代わって、このパラメータの更新は、図３９に示すように世代数の適応度の変化から、変化の割合が少なくなると終了してもよい。
【０１０２】
以上説明したように、本実施形態によれば、浅部及び単独の埋設物からの反射波群の特徴である双曲線パターンと、反射波の振幅下降が干渉波の影響を受けにくいという特徴に注目した埋設物の位置推定を行い、しかも推定処理用パラメータを遺伝的アルゴリズムを用いて適応制御し、その結果をもとに内部構造の３次元の画像を生成して出力した。これにより、高い精度での構造物内部の３次元構成の画像を生成することができ、この結果により工事の際に必要な構造物内部の情報を得ることができる。従って、埋設物探査処理を実行するときに用いる測定パラメータを最適値に適応制御しながら埋設物探査処理を実行することができ、種々の探査物体中で埋設物を容易にかつ高精度で探索し、誰でも容易に埋設物の位置を視認により確認することができる。
【０１０３】
以上の第２の実施形態においては、連結探査処理（ステップＳ６）を実行した後、埋設物抽出処理（ステップＳ７）を実行しているが、本発明はこれに限らず、後者の埋設物抽出処理を実行しなくてもよい。
【０１０４】
以上の第２の実施形態においては、ステップＳ４においてＸＺスライスとＹＺスライスの２つの断面の推定処理結果のデータを３次元空間上に合成して配置するように仮想的にアドレス割り当てして画像メモリ２３に記憶しているが、本発明はこれに限らず、ＸＺスライスとＹＺスライスのうちの少なくとも１つの断面の推定処理結果のデータを３次元空間上に合成して配置するように仮想的にアドレス割り当てして画像メモリ２３に記憶してもよい。すなわち、１つのスライスの断面画像のみに基づいて埋設物の探査の処理を行ってもよい。
【０１０５】
【実施例】
図４０乃至図４２は、それぞれ図２の埋設物探査システムの第１乃至第３の実施例における探索結果を示す図であって、これらの図の（ａ）は従来技術によるＸ線画像の測定結果を示す図であり、（ｂ）本実施形態に係る方法を適応する前の探索結果画像を示す図であり、（ｃ）は本実施形態に係る方法を適応した後の探索結果画像を示す図である。なお、図４０乃至図４２の（ｂ）及び（ｃ）に図示した結果中の破線（四角）はＸ線画像に対応する部分を示している。
【０１０６】
図４０乃至図４２から明らかなように、従来法では３次元表示の際に、システムの１４個の推定処理用パラメータを予め固定していたために、破線部分の埋設管が全く検出されていなかったが、図４０乃至図４２の楕円形状の破線で示すように、本実施形態に係る方法を適用することにより内部パラメータが自動的に最適化されたことで、従来法で検出されていなかった埋設管を検出できた。
【０１０７】
＜変形例＞
以上の実施形態においては、図３の埋設物探査画像処理のプログラムデータをＣＤ−ＲＯＭ４５ａに格納して実行するときにプログラムメモリ２４にロードして実行しているが、本発明はこれに限らず、ＣＤ−Ｒ、ＣＤ−ＲＷ、ＤＶＤ、ＭＯなどの光ディスク又は光磁気ディスクの記録媒体、もしくは、フロッピーディスクなどの磁気ディスクの記録媒体など種々の記録媒体に格納してもよい。これらの記録媒体は，コンピュータで読み取り可能な記録媒体である。また、図３の埋設物探査画像処理のプログラムデータを予めプログラムメモリ２４に格納して当該画像処理を実行してもよい。
【０１０８】
以上の実施形態においては、探査物体中において所定の長さ方向で連続して延在する埋設物を、電磁波の送信波を探査物体の表面から放射し、上記送信波の反射波を受信し、反射波のデータに基づいて探査する埋設物探査処理方法及び装置について説明しているが、本発明はこれに限らず、電磁波に代えて超音波を用いて埋設物の探索処理を同様に実行してもよい。
【０１０９】
以上の実施形態においては、遺伝的アルゴリズムを用いた位置推定処理用パラメータの適応制御処理において、評価、選択、交叉、突然変異、判定の各処理について一例を示しているが、本発明はこれに限られるものではない。
【０１１０】
【発明の効果】
以上詳述したように本発明に係る適応制御型測定処理方法又は装置によれば、測定された入力データを受信し、測定時に用いる測定パラメータの初期値を設定し、上記受信した入力データに基づいて設定された測定パラメータを用いて所定の測定処理を実行することにより測定処理結果を得て、上記得られた測定処理結果に基づいて遺伝的アルゴリズムを用いて、上記測定パラメータの適合度が高くなるように測定パラメータを適応制御して設定し、当該設定処理の後に所定の終了条件を満たしているか否かを判断し、上記終了条件を満たしていないとき上記の測定処理及び設定処理を繰り返す一方、上記終了条件を満たしているとき、最後の測定処理における測定処理結果を出力データとして出力する。従って、上記測定処理を実行するときに用いる測定パラメータを最適値に適応制御しながら測定処理を実行することができ、従来技術に比較してより高精度で正確に測定処理を実行することができる。
【０１１１】
また、本発明に係る埋設物探査処理方法又は装置によれば、探査物体中において所定の長さ方向で連続して延在する埋設物を、電磁波又は超音波の送信波を探査物体の表面から放射し、上記送信波の反射波を受信し、反射波のデータに基づいて探査する埋設物探査処理方法又は装置において、上記受信した反射波のデータに基づいて、上記埋設物の探査のための所定の特徴パラメータのデータを抽出し、上記抽出された特徴パラメータのデータに基づいて、遺伝的アルゴリズムを用いて、位置推定処理用パラメータの適合度が高くなるように位置推定処理用パラメータを適応制御しながら、上記埋設物が連続して延在するという知識を用いて上記埋設物の位置を推定し、上記推定された埋設物の位置に基づいて、上記埋設物の画像を３次元画像の形式で生成して出力する。従って、埋設物探査処理を実行するときに用いる測定パラメータを最適値に適応制御しながら埋設物探査処理を実行することができ、種々の探査物体中で埋設物を容易にかつ、従来技術に比較して高精度で探索し、誰でも容易に埋設物の位置を視認により確認することができる。
【０１１２】
さらに、本発明に係る埋設物探査処理方法又は装置によれば、探査物体中において所定の長さ方向で連続して延在する埋設物を、電磁波又は超音波の送信波を探査物体の表面から放射し、上記送信波の反射波を受信し、反射波のデータに基づいて探査する埋設物探査処理方法又は装置において、上記受信した反射波のデータに基づいて、上記探査物体の表面とは直交する少なくとも１つの断面の濃度データを有する画像データを生成し、上記埋設物が連続して延在するという知識を用いて上記生成した画像データから埋設物が存在しない画像データを除去することにより上記埋設物の候補点を示す少なくとも１つの断面の候補点画像データを生成し、上記生成された少なくとも１つの断面の候補点画像データを３次元空間上に仮想的に配置するように画像メモリに格納し、上記画像メモリに格納された候補点画像データに基づいて、遺伝的アルゴリズムを用いて、位置推定処理用パラメータの適合度が高くなるように位置推定処理用パラメータを適応制御しながら、上記埋設物が連続して延在するという知識を用いて候補点を連結することにより上記埋設物の位置を推定し、上記推定された埋設物の位置に基づいて、上記埋設物の画像を３次元画像の形式で生成して出力する。従って、埋設物探査処理を実行するときに用いる測定パラメータを最適値に適応制御しながら埋設物探査処理を実行することができ、種々の探査物体中で埋設物を容易にかつ、従来技術に比較して高精度で探索し、誰でも容易に埋設物の位置を視認により確認することができる。
【図面の簡単な説明】
【図１】本発明に係る第１の実施形態である測定パラメータの適応制御処理を備えた測定処理を示すフローチャートである。
【図２】本発明に係る第２の実施形態である埋設物探査システムの構成を示すブロック図である。
【図３】図２の画像処理装置１０のＣＰＵ２０によって実行される埋設物探査画像処理（メインルーチン）を示すフローチャートである。
【図４】図３のサブルーチンであるＸ方向の走査時の反射波信号データに基づく埋設位置候補推定処理（ステップＳ２）を示すフローチャートである。
【図５】図３のサブルーチンであるＹ方向の走査時の反射波信号データに基づく埋設位置候補推定処理（ステップＳ３）を示すフローチャートである。
【図６】図４のサブルーチンである埋設位置候補のＸＺスライスの画像データに基づいて濃淡の極小点探査により埋設位置候補を推定する処理（ステップＳ１５）を示すフローチャートである。
【図７】図６のサブルーチンである濃淡の極小点探査により埋設位置候補を推定する処理（ステップＳ７１）を示すフローチャートである。
【図８】図６のサブルーチンである双曲線パターンを見ることにより埋設位置候補を推定する処理（ステップＳ７２）を示すフローチャートである。
【図９】図６のサブルーチンである上記２つの埋設位置候補を推定する処理を重み付けにより統合させる処理（ステップＳ７３）を示すフローチャートである。
【図１０】図３のサブルーチンである連結探査処理（ステップＳ５）の第１の部分を示すフローチャートである。
【図１１】図３のサブルーチンである連結探査処理（ステップＳ５）の第２の部分を示すフローチャートである。
【図１２】図３のサブルーチンである埋設物抽出処理（ステップＳ６）を示すフローチャートである。
【図１３】図３のサブルーチンである遺伝的アルゴリズムを用いて推定処理用パラメータを適応制御して設定する処理（ステップＳ８）を示すフローチャートである。
【図１４】図２の埋設物探査システムにおいて用いる送信波信号の周波数スペクトルを示すスペクトル図である。
【図１５】図１４の送信波信号の送信パルスの波形を示す信号波形図である。
【図１６】図２の埋設物探査システムで用いる探査の走査方法を示す平面図である。
【図１７】図２の埋設物探査システムで用いる埋設物探査用送受信装置を備えた探査装置の測定原理を示す正面図である。
【図１８】図２の埋設物探査システムによって測定された反射波群の双曲線パターンを示す図及び画像の写真である。
【図１９】（ａ）は図２の埋設物探査システムによって測定された受信波形の波形図であり、（ｂ）はそのＸ方向の探査結果画像の写真であり、（ｃ）はそのＹ方向の探査結果画像の写真である。
【図２０】（ａ）は図２の埋設物探査システムによって測定された探査結果画像の写真であり、（ｂ）は（ａ）におけるＬａ線上の波形を示す波形図である。
【図２１】（ａ）は図２の埋設物探査システムによって測定された探査結果画像の写真であり、（ｂ）は（ａ）のＬｂ線上の波形の波形図であり、（ｃ）はその相互干渉部分の拡大図である。
【図２２】図２の埋設物探査システムによって測定された濃度分布を示すグラフである。
【図２３】図２の埋設物探査システムによって測定された候補領域の抽出結果を示す図であって、（ａ）はその原画像の写真であり、（ｂ）はその処理結果の画像を示す図である。
【図２４】（ａ）及び（ｂ）はそれぞれ図１の埋設物探査システムによって測定された信号波形を示す波形図であり、（ｃ）は処理前の画像を示す図であり、（ｄ）は処理後の画像を示す図である。
【図２５】（ａ）は図２の埋設物探査システムによって測定されたラベリング結果の画像を示す図であり、（ｂ）はその埋設位置推定結果の画像を示す図である。
【図２６】図２の埋設物探査システムによって測定された図１９の領域Ｎ４の概略を示す３次元の斜視図である。
【図２７】図２の埋設物探査システムによって測定された、極小点探査により埋設位置を推定できない例を示す画像の図及び３次元の斜視図である。
【図２８】図２の埋設物探査システムによって実行された推定位置の連結処理を示す斜視図である。
【図２９】図２の埋設物探査システムにおいて用いる埋設管抽出のためのメンバーシップ関数であって、（ａ）は外周からの距離に関するメンバーシップ関数のグラフであり、（ｂ）は深度に関するメンバーシップ関数のグラフである。
【図３０】図２の埋設物探査システムにおいて用いる濃度の特徴を示す図であって、（ａ）は埋設管の場合における調査距離に対する伝搬時間を示した、電磁波の反射波群に係る探査結果の濃淡画像と、その濃淡画像における調査距離に対する濃度を示すグラフとを示す図であり、（ｂ）は干渉領域の場合における調査距離に対する伝搬時間を示した、電磁波の反射波群に係る探査結果の濃淡画像と、その濃淡画像における調査距離に対する濃度を示すグラフとを示す図である。
【図３１】図２の埋設物探査システムにおいて用いる濃度の知識のメンバーシップ関数であって、（ａ）は入力のメンバーシップ関数のグラフであり、（ｂ）は出力のメンバーシップ関数のグラフである。
【図３２】図２の埋設物探査システムにおいて用いる双曲線形状の特徴を示す図であって、（ａ）は調査距離に対する伝搬時間を示した、電磁波の反射波群に係る探査結果の濃淡画像を示す図であり、（ｂ）はその探査結果の濃淡画像における双曲線形状を示す図であり、（ｃ）はその理論双曲線を示す図である。
【図３３】図２の埋設物探査システムにおいて用いる双曲線形状の知識のメンバーシップ関数であって、（ａ）は入力のメンバーシップ関数のグラフであり、（ｂ）は出力のメンバーシップ関数のグラフである。
【図３４】図２の埋設物探査システムにおいて用いる深さに対する図９のステップＳ８５の処理で用いる重み付け係数ｗ_ｉ，ｗ_ｈの関数を示すグラフである。
【図３５】図１３の評価処理（ステップＳ９１）における、適合度を用いた評価方法を示す図であって、（ａ）は適合度が低いときの埋設管の探索結果画像を示す図であり、（ｂ）は適合度が高いときの埋設管の探索結果画像を示す図である。
【図３６】図１３の選択処理（ステップＳ９２）におけるルーレット選択法を用いた選択処理を示す図であって、（ａ）は選択処理における各個体のデータの一例を示す図であり、（ｂ）は上記各個体に対する適合度ｇを示す図であり、（ｃ）は上記各個体に対する選択確率Ｐを示す図であり、（ｄ）は当該ルーレット選択の確率の円グラフを示す図である。
【図３７】図１３の交叉処理（ステップＳ９３）における一点交叉処理の一例を示す図であり、（ａ）は当該交叉処理前における選択された２つの固体のデータを示す図であり、（ｂ）は当該交叉処理後における選択された２つの固体のデータを示す図である。
【図３８】図１３の突然変異処理（ステップＳ９３）における処理の一例を示す図である。
【図３９】図２の埋設物探査システムの実施例における世代数Ｔに対する適合度の推移を示すグラフである。
【図４０】図２の埋設物探査システムの第１の実施例における探索結果を示す図であって、（ａ）は従来技術によるＸ線画像の測定結果を示す図であり、（ｂ）本実施形態に係る方法を適応する前の探索結果画像を示す図であり、（ｃ）は本実施形態に係る方法を適応した後の探索結果画像を示す図である。
【図４１】図２の埋設物探査システムの第２の実施例における探索結果を示す図であって、（ａ）は従来技術によるＸ線画像の測定結果を示す図であり、（ｂ）本実施形態に係る方法を適応する前の探索結果画像を示す図であり、（ｃ）は本実施形態に係る方法を適応した後の探索結果画像を示す図である。
【図４２】図２の埋設物探査システムの第３の実施例における探索結果を示す図であって、（ａ）は従来技術によるＸ線画像の測定結果を示す図であり、（ｂ）本実施形態に係る方法を適応する前の探索結果画像を示す図であり、（ｃ）は本実施形態に係る方法を適応した後の探索結果画像を示す図である。
【符号の説明】
１…埋設物探査用電磁波送受信装置、
１Ｒ…車輪、
１ＲＲ…車体、
２…送信アンテナ、
３…受信アンテナ、
４…送信機、
５…受信機、
６…コントローラ、
７…通信インターフェース、
１０…画像処理装置、
２０…ＣＰＵ、
２１…ＲＯＭ、
２２…ＲＡＭ、
２３…画像メモリ、
２４…プログラムメモリ、
２５…処理メモリ、
２６…受信メモリ、
３０…バス、
３１…キーボードインターフェース、
３２…マウスインターフェース、
３３…ディスプレイインターフェース、
３４…プリンタインターフェース、
３５…ドライブ装置インターフェース、
４１…キーボード、
４２…マウス、
４３…ＣＲＴディスプレイ、
４４…プリンタ、
４５…ＣＤ−ＲＯＭドライブ装置、
４５ａ…ＣＤ−ＲＯＭ、
５１…通信ケーブル、
６１…通信インターフェース。
９０…埋設物、
９１…探査物体、
９２…探査面。[0001]
TECHNICAL FIELD OF THE INVENTION
The present invention provides an adaptive control type measurement processing method and apparatus for adaptively controlling a predetermined measurement parameter, for example, a buried object exploration processing method and apparatus for searching for an buried object in an exploration object such as concrete or underground. The present invention relates to an adaptive control type measurement processing program using a control type measurement processing method, a buried object search processing program using the above buried object search processing method, and a recording medium on which these programs are recorded.
[0002]
[Prior art]
In recent years, the undergrounding of gas, water, communication and power cables, etc. has been promoted, but since the drawings that accurately record the position of such existing buried pipes have not been sufficiently prepared, damages in road construction etc. Has occurred. In addition, in the case of penetration of concrete slabs and walls by adding electrical equipment, information and communication equipment, and air conditioning and sanitary facilities to existing buildings due to an increase in the number of renovation construction starts, water pipes, gas pipes, conduit pipes, etc. Damage accidents such as are increasing. For this reason, in order to prevent such accidents beforehand, it is necessary to know in advance the internal structure of the surrounding area where construction is to be carried out. It's being used.
[0003]
For example, in the electromagnetic wave method, electromagnetic waves are radiated from a transmitting antenna to the ground or a structure and propagated, and a reflected wave when the object strikes an object having a different electric constant such as a dielectric constant is measured by a receiving antenna. Since the reflected signal is radiated at an angle and moves through the antenna to receive the reflected signal, the reflected image of the buried pipe becomes hyperbolic in the cross-sectional image, and signals of other buried objects and the like are also superimposed. It is very difficult to discriminate between a buried pipe and a non-buried pipe, which requires skilled knowledge, and there is a problem that the operator needs to be an experienced person.
[0004]
In addition, the ultrasonic method is characterized by the fact that ultrasonic waves are reflected regardless of whether they are metallic or non-metallic, that deep burial positions can be searched, that there is no adverse effect on the human body, and that they are easy to handle. It is drawing attention as a next-generation nondestructive inspection technology. However, although nondestructive inspection using ultrasonic waves is widely used as a flaw detection method (crack detection) for a specimen, it has not been widely applied to estimating a piping configuration in concrete. This is because concrete is scattered and attenuated due to its porosity, which is not constant due to the heterogeneity inside the concrete, which makes waveform analysis difficult. As described above, since the scattering and attenuation are not constant inside the concrete, there is a problem that it is very difficult to compare the amplitude value itself of the received waveform.
[0005]
In order to solve the above problems, the present inventor has disclosed in Japanese Patent Application Laid-Open No. 2000-338255, "A buried object continuously extending in a predetermined length direction in an exploration object is subjected to electromagnetic waves or ultrasonic waves. A buried object exploration processing method for radiating a transmission wave from the surface of an exploration object, receiving a reflected wave of the transmitted wave, and performing exploration based on data of the reflected wave, wherein the embedding is performed based on the received reflected wave data Extracting data of a predetermined characteristic parameter for exploring an object; and, based on the data of the extracted characteristic parameter, the position of the embedded object using knowledge that the embedded object extends continuously. And a method for generating and outputting an image of the buried object in the form of a three-dimensional image based on the estimated position of the buried object. " That.
[0006]
Specifically, the buried object detection system using this buried object detection processing method scans the X-axis direction and the Y-axis direction, which are orthogonal to each other, at regular intervals using a pulse radar sensor. A tomographic image of 256 gradations is acquired based on the input image, input data is configured from a plurality of scanned images, and as a result of applying an analysis algorithm, the output data is expressed in three dimensions. The position of the buried pipe is determined by taking the buried pipe data obtained from the input image into the system and applying an extraction algorithm to obtain measurement parameters related to the knowledge of the expert such as concentration and hyperbolic shape, as well as the burial depth and surrounding conditions. It is estimated by performing a calculation belonging to a buried pipe from measurement parameters related to the priority of knowledge. Specifically, these measurement parameters include a parameter relating to knowledge of concentration, a parameter relating to a hyperbolic shape, a parameter relating to an estimated position of a buried pipe, and the like, and these measurement parameters are empirically set in advance based on experiments so far. Have been.
[0007]
[Problems to be solved by the invention]
The conventional buried pipe estimation processing implemented in this buried object exploration system is a processing in the direction of suppressing the estimation of buried pipes, and in addition, the system fixes the parameters to preferable values under the same exploration environment Therefore, there is a problem that a buried pipe unextracted phenomenon appears depending on the exploration environment. In that case, it is necessary to manually adjust and search for unextracted buried pipes.However, since there are multiple measurement parameters and their combination becomes enormous, the measurement parameters cannot be set to optimal values, and the conventional accuracy Could not be obtained. In addition, the relationship between the parameters and the number of buried pipes extracted is non-linear, and there is a problem that the optimum value of the measurement parameter cannot be easily determined.
[0008]
An object of the present invention is to solve the above problems, and to perform an adaptive control type measurement processing method and apparatus capable of executing a measurement process while adaptively controlling a measurement parameter used when executing a predetermined measurement process to an optimum value. An object of the present invention is to provide an adaptive control type measurement processing program and a recording medium on which the program is recorded.
[0009]
Another object of the present invention is to solve the above-described problems, and to perform a buried object search process while adaptively controlling a measurement parameter used when executing a buried object search process to an optimum value. A buried object search processing method and apparatus, a buried object search processing program, and a program for easily searching for a buried object in an exploration object with high accuracy and enabling anyone to easily confirm the position of the buried object visually. An object of the present invention is to provide a recorded recording medium.
[0010]
[Means for Solving the Problems]
An adaptive control type measurement processing method according to the present invention includes a first step of receiving measured input data,
A second step of setting initial values of measurement parameters used at the time of measurement;
A third step of performing a predetermined measurement process using a measurement parameter set based on the received input data to obtain a measurement process result;
A fourth step of adaptively controlling and setting the measurement parameters so that the degree of conformity of the measurement parameters is increased using a genetic algorithm based on the obtained measurement processing results;
After the fourth step, it is determined whether or not a predetermined end condition is satisfied. When the end condition is not satisfied, the third and fourth steps are repeated, while when the end condition is satisfied, And a fifth step of outputting the measurement processing result in the last third step as output data.
[0011]
Further, the embedded object exploration processing method according to the present invention, the embedded object continuously extending in the predetermined length direction in the exploration object, radiating the electromagnetic wave or ultrasonic transmission wave from the surface of the exploration object, In a buried object detection processing method of receiving a reflected wave of a transmission wave and searching based on data of the reflected wave,
Based on the received reflected wave data, extracting predetermined characteristic parameter data for the search for the buried object,
Based on the extracted feature parameter data, using a genetic algorithm, the embedded object is continuously controlled while adaptively controlling the position estimation processing parameter so that the degree of conformity of the position estimation processing parameter is increased. Estimating the position of the buried object using the knowledge of extending;
Generating and outputting an image of the buried object in the form of a three-dimensional image based on the estimated position of the buried object.
[0012]
Further, the embedded object exploration processing method according to the present invention, the embedded object continuously extending in a predetermined length direction in the exploration object, radiating the transmission wave of the electromagnetic wave or the ultrasonic wave from the surface of the exploration object, In a buried object detection processing method of receiving a reflected wave of a transmission wave and searching based on data of the reflected wave,
Based on the received reflected wave data, generate image data having density data of at least one cross section orthogonal to the surface of the exploration object, and use the knowledge that the buried object extends continuously. Generating candidate point image data of at least one cross section indicating candidate points of the buried object by removing image data having no buried object from the generated image data;
Storing the generated candidate point image data of at least one cross section in an image memory so as to be virtually arranged in a three-dimensional space;
Based on the candidate point image data stored in the image memory, using a genetic algorithm, while adaptively controlling the position estimation processing parameters so as to increase the degree of conformity of the position estimation processing parameters, the embedded object is Estimating the position of the buried object by connecting the candidate points using the knowledge that it extends continuously;
Generating and outputting an image of the buried object in the form of a three-dimensional image based on the estimated position of the buried object.
[0013]
In the embedded object exploration processing method, preferably, after the step of estimating the position of the embedded object,
(A) a first rule that the probability of being a buried object is high when both ends of the buried object are close to the end of the sought object and the depth of the buried object is not located between the layers of the sought object;
(B) a second rule that the probability that the object is a buried object is low when both ends of the buried object are far from the end of the object to be searched and the depth of the object is located between the cover and the thickness of the object to be searched. Using fuzzy rules,
Based on the estimated position of the buried object, it is determined whether the estimated position of the buried object is a buried object, and when it is determined that the buried object is not a buried object, the position of the estimated buried object is determined. The method further comprises the step of removing from the data.
[0014]
Further, in the embedded object exploration processing method, the step of estimating a position of the embedded object is preferably
The knowledge of the density in the candidate point image data stored in the image memory is expressed, and the degree of the embedding position candidate based on the average angle of the density change is determined using a predetermined first membership function indicating the degree of the embedding position candidate. Calculating a degree;
Represents the knowledge of the hyperbolic shape in the candidate point image data stored in the image memory, and indicates the degree of the embedding position candidate based on the degree of coincidence between the hyperbola and the theoretical hyperbola obtained by referring to the density in the candidate point image data. Calculating the degree of the second buried object using a predetermined second membership function as shown;
A degree of a third buried object is calculated by integrating the calculated degree of the first buried object and the calculated degree of the second buried object by a linear combination using a function of a predetermined weighting coefficient. Steps and
Estimating the position of the buried object by determining whether or not the image data is a buried object candidate based on the degree of the third buried object.
[0015]
Further, in the embedded object exploration processing method, the position estimation processing parameter preferably includes a parameter for defining the first membership function and a parameter for defining the second membership function. , And a parameter for defining a function of the weighting coefficient.
[0016]
Still further, in the above-described buried object exploration processing method, the degree of conformity is preferably represented by a sum of lengths of buried pipes estimated and extracted from candidate point image data stored in the image memory. Features.
[0017]
The adaptive control type measurement processing device according to the present invention, receiving means for receiving the measured input data,
Setting means for setting initial values of measurement parameters used at the time of measurement;
Measuring means for obtaining a measurement processing result by executing a predetermined measurement processing using a measurement parameter set based on the received input data,
Using a genetic algorithm based on the obtained measurement processing results, adaptive control means for adaptively controlling and setting the measurement parameters so that the degree of conformity of the measurement parameters is high,
After the processing of the adaptive control means, it is determined whether a predetermined end condition is satisfied. When the end condition is not satisfied, the processing of the measuring means and the processing of the adaptive control means are repeated, while And a result output means for outputting, as output data, a measurement processing result in the processing of the last measurement means when the condition is satisfied.
[0018]
Further, the embedded object exploration processing apparatus according to the present invention, the embedded object continuously extending in a predetermined length direction in the exploration object, radiating the electromagnetic wave or ultrasonic transmission wave from the surface of the exploration object, In a buried object exploration processing device that receives the reflected wave of the transmitted wave and performs exploration based on the data of the reflected wave,
Based on the received reflected wave data, extracting means for extracting data of predetermined characteristic parameters for exploration of the buried object,
Based on the extracted feature parameter data, using a genetic algorithm, the embedded object is continuously controlled while adaptively controlling the position estimation processing parameter so that the degree of conformity of the position estimation processing parameter is increased. Estimating means for estimating the position of the buried object using the knowledge of extending,
Output means for generating and outputting an image of the buried object in the form of a three-dimensional image based on the estimated position of the buried object.
[0019]
Further, the embedded object exploration processing apparatus according to the present invention, the embedded object continuously extending in the predetermined length direction in the exploration object, radiating the electromagnetic wave or ultrasonic transmission wave from the surface of the exploration object, the above In a buried object exploration processing device that receives the reflected wave of the transmitted wave and performs exploration based on the data of the reflected wave,
Based on the received reflected wave data, generate image data having density data of at least one cross section orthogonal to the surface of the exploration object, and use the knowledge that the buried object extends continuously. Generating means for generating candidate point image data of at least one cross section indicating a candidate point of the buried object by removing image data having no buried object from the generated image data;
Storage means for storing the generated candidate point image data of at least one section in an image memory so as to be virtually arranged in a three-dimensional space;
Based on the candidate point image data stored in the image memory, using a genetic algorithm, while adaptively controlling the position estimation processing parameters so as to increase the degree of conformity of the position estimation processing parameters, the embedded object is Position estimating means for estimating the position of the buried object by connecting the candidate points using the knowledge of extending continuously,
Output means for generating and outputting an image of the buried object in the form of a three-dimensional image based on the estimated position of the buried object.
[0020]
In the buried object exploration processing device, preferably, after the processing of the position estimating means,
(A) a first rule that the probability of being a buried object is high when both ends of the buried object are close to the end of the sought object and the depth of the buried object is not located between the layers of the sought object;
(B) a second rule that the probability that the object is a buried object is low when both ends of the buried object are far from the end of the object to be searched and the depth of the object is located between the cover and the thickness of the object to be searched. Using fuzzy rules,
Based on the estimated position of the buried object, it is determined whether the estimated position of the buried object is a buried object, and when it is determined that the buried object is not a buried object, the position of the estimated buried object is determined. It is characterized by further comprising a removing means for removing from data.
[0021]
Further, in the embedded object exploration processing device, the position estimating means is preferably
The knowledge of the density in the candidate point image data stored in the image memory is expressed, and the degree of the embedding position candidate based on the average angle of the density change is determined using a predetermined first membership function indicating the degree of the embedding position candidate. First calculating means for calculating the degree;
Represents the knowledge of the hyperbolic shape in the candidate point image data stored in the image memory, and indicates the degree of the embedding position candidate based on the degree of coincidence between the hyperbola and the theoretical hyperbola obtained by referring to the density in the candidate point image data. Second calculating means for calculating the degree of the second buried object using a predetermined second membership function shown;
A degree of a third buried object is calculated by integrating the calculated degree of the first buried object and the calculated degree of the second buried object by a linear combination using a function of a predetermined weighting coefficient. A third calculating means;
A buried object position estimating means for estimating the position of the buried object by determining whether or not the data is image data of a buried object candidate based on the degree of the third buried object.
[0022]
Further, in the embedded object exploration processing device, the position estimation processing parameter preferably includes a parameter for defining the first membership function and a parameter for defining the second membership function. , And a parameter for defining a function of the weighting coefficient.
[0023]
Still further, in the embedded object exploration processing device, the fitness is preferably represented by the sum of the lengths of the respective embedded pipes estimated and extracted from the candidate point image data stored in the image memory. Features.
[0024]
An adaptive control type measurement processing program according to the present invention is characterized by including each step of the above adaptive control type measurement processing method.
[0025]
Further, a buried object search processing program according to the present invention is characterized in that it includes each step of the above-mentioned buried object search processing method.
[0026]
Further, a recording medium according to the present invention is characterized in that the above-described adaptive control type measurement processing program is recorded.
[0027]
Still further, a recording medium according to the present invention is characterized by recording the above-mentioned buried object search processing program.
[0028]
BEST MODE FOR CARRYING OUT THE INVENTION
Hereinafter, embodiments of the present invention will be described with reference to the drawings.
[0029]
<First embodiment>
FIG. 1 is a flowchart showing a measurement process including an adaptive control process of a measurement parameter according to the first embodiment of the present invention. The first embodiment is characterized in that in a predetermined measurement process executed using a predetermined measurement parameter, the measurement process is executed while adaptively controlling the measurement parameter. Here, the measurement process is executed by a computer such as a digital computer, and can be configured as a measurement device that executes the measurement process. In addition, the measurement process is widely applied to a measurement process related to a physical phenomenon, a measurement process related to an electric system or a power distribution system, a measurement process related to a telecommunication system, etc., in addition to the embedded object exploration process described in the second embodiment. can do.
[0030]
In FIG. 1, first, input data measured in step S101 is received, and in step S102, initial values of measurement parameters are generated and set. Next, based on the input data measured in step S103, a predetermined measurement process is performed using the set measurement parameters to obtain a measurement process result. Then, in step S104, using a genetic algorithm based on the measurement processing result, the measurement parameters are adaptively controlled and set so as to increase the degree of conformity of the measurement parameters, and in step S105, a predetermined termination condition is satisfied. If the end condition is not satisfied, the process returns to step S103, and the above process is repeated. If the end condition is satisfied, the measurement process result in the last step S103 is output as output data in step S106. Is output, and the measurement process ends.
[0031]
Here, as the termination condition, if the number of repetitions of the processing from step S103 to step S104 is counted and reaches a predetermined number, the processing may be terminated, or the difference between the parameter values before and after the adaptive control of the measurement parameter may be performed. May be smaller than a predetermined value and the process may be terminated on the assumption that the convergence state has been reached. In addition, the degree of conformity is such that the higher the degree of conformity, the higher the measurement accuracy or accuracy of the measurement process, while the lower the degree of conformity, the lower the measurement accuracy or precision of the measurement process. It is a defined scale.
[0032]
Each step of the measurement process in FIG. 1 is generated as a program, and the program is stored in a storage device of the computer and then executed by the computer. Alternatively, each step of the measurement process of FIG. 1 is generated as a program, and the program is recorded on a computer-readable recording medium such as a CD-ROM, a CD-R, or a DVD, and the recorded program is stored in a storage device of the computer. And then executed by the computer.
[0033]
As described above, according to the first embodiment, it is possible to execute a measurement process while adaptively controlling a measurement parameter used when executing a predetermined measurement process to an optimum value. It can be executed with high precision to increase the degree.
[0034]
<Second embodiment>
FIG. 2 is a block diagram showing a configuration of an embedded object exploration system according to a second embodiment of the present invention, and FIG. 3 is an embedded object exploration image processing executed by the CPU 20 of the image processing apparatus 10 in FIG. It is a flowchart which shows a (main routine).
[0035]
The embedded object exploration system of this embodiment emits an electromagnetic wave transmitted from the surface of the object, and receives a reflected wave of the transmitted wave. Then, exploration is performed based on the reflected wave data. Here, the image processing apparatus 10 can easily and highly accurately search by executing the embedded object exploration image processing shown in FIG. 3, and in the form of a three-dimensional image indicating the position of the embedded object, However, it is characterized in that the position of the buried object can be easily confirmed visually. In the present embodiment, the buried object is, for example, a tube shape that is continuously buried in a predetermined direction, such as a steel pipe, a CD pipe (cable pipe), a copper pipe, a reinforcing bar, etc., buried in concrete or the ground. It is intended for conduits, gas pipes, water pipes, etc. which are buried pipes. The buried pipe may extend linearly and continuously in the exploration object, or may be curved and extend continuously.
[0036]
As shown in FIG. 2, the image processing system according to the present embodiment is roughly divided into:
(A) After radiating a transmission wave signal of an electromagnetic wave for exploring an embedded object from the surface of an object to be detected, receiving a reflected wave signal of the transmitted wave and transmitting the data to the image processing apparatus 10 An electromagnetic wave transmitting / receiving device (hereinafter, referred to as an electromagnetic wave transmitting / receiving device) 1;
(B) It is constituted by a digital computer, and by executing the embedded object exploration image processing shown in FIG. 3 based on the received reflected wave signal data, it is possible to search easily and with high accuracy. It is displayed or printed in the form of a three-dimensional image indicating the position of an object and output.
[0037]
As shown in FIG. 23, the electromagnetic wave transmitting / receiving apparatus 1 of FIG. 2 is mounted on a vehicle body 1RR having a motor R (not shown) or a wheel R1 driven and rotated manually, and a search surface 92 which is a surface of a search object 91. As described above, the object to be searched is scanned while being moved at predetermined intervals Δx and Δy in two directions orthogonal to each other as shown in FIG. The direction orthogonal to the search surface 92 is defined as the Z direction, and the distance from the search surface 92 is defined as the burial depth. The electromagnetic wave transmitting and receiving apparatus 1 includes a transmitter 4 that generates and amplifies a transmission wave signal of an electromagnetic wave having a predetermined frequency spectrum, and transmits and amplifies the transmission wave signal to a search object via a transmission antenna 2; And a receiver 5 which receives and amplifies the reflected wave signal from the receiver using the receiving antenna 3, samples the reflected signal at a predetermined time timing, and outputs the sampled signal as reflected wave data. Further, the electromagnetic wave transmitting / receiving device 1 has a controller 6 for controlling the operations of the transmitter 4 and the receiver 5 by generating and outputting a timing signal indicating the transmission timing of the transmitter 4 and the reception timing of the receiver 5. And a communication interface 7 for executing signal processing such as signal conversion. The data of the reflected wave signal output from the receiver 5 is transmitted to the image processing device 10 via the communication interface 7 and the communication cable 51. Here, these communication interfaces 7 and 61 are communication interfaces having a predetermined communication protocol and signal format, for example.
[0038]
Next, the configuration of the image processing apparatus 10 will be described with reference to FIG. The image processing device 10
(A) a CPU (Central Processing Unit) 20 of a computer that calculates and controls the operation and processing of the image processing device 10;
(B) a ROM (read only memory) 21 for storing a basic program such as an operation program and data necessary for executing the basic program;
(C) a RAM (random access memory) 22 which operates as a working memory of the CPU 20 and temporarily stores parameters and data necessary for image processing;
(D) a reception memory 26 which is composed of, for example, a hard disk memory and stores data of a reflected wave signal received from the electromagnetic wave transmission / reception device 1;
(E) a processing memory 25 which is constituted by, for example, a hard disk memory and temporarily stores data being processed when the embedded object exploration image processing of FIG. 3 is executed;
(F) An image memory 23 which is constituted by, for example, a hard disk memory and temporarily stores image data when executing the embedded object exploration image processing of FIG.
(G) a program memory 24 which is composed of, for example, a hard disk memory and stores a program for the embedded object exploration image processing of FIG. 3 read using the CD-ROM drive device 45;
(H) a communication interface 61 connected to the communication interface 7 of the electromagnetic wave transmitting / receiving device 1 for transmitting and receiving data to and from the communication interface 7;
(I) A keyboard connected to a keyboard 41 for inputting predetermined data and instruction commands, receiving data and instruction commands input from the keyboard 41, performing interface processing such as predetermined signal conversion, and transmitting the data to the CPU 20. An interface 31,
(J) Connected to a mouse 42 for inputting an instruction command on the CRT display 43, receives data and an instruction command input from the mouse 42, performs an interface process such as a predetermined signal conversion, and transmits the signal to the CPU 20. A mouse interface 32,
(K) Connected to a CRT display 43 that displays image data processed by the CPU 20, a setting instruction screen, and the like, converts image data to be displayed into image signals for the CRT display 43, and outputs to the CRT display 43 for display. A display interface 33 for
(L) A printer interface 34 connected to a printer 44 that prints image data processed by the CPU 20 and predetermined analysis results and the like, performs predetermined signal conversion of print data to be printed, and outputs to the printer 44 for printing. When,
(M) The program is connected to a CD-ROM drive 45 for reading out the program data of the image processing program from the CD-ROM 45a in which the embedded object search image processing program is stored, and stores the read program data of the embedded object search image processing program in a predetermined manner. And a drive device interface 35 for performing signal conversion or the like and transferring the signal to the program memory 24. These circuits 20-26, 31-35 and 61 are connected via the bus 30.
[0039]
Next, a measuring method will be described. In the present embodiment, the search is performed using an electromagnetic wave including a broadband frequency component. FIG. 14 shows a frequency spectrum of the electromagnetic wave transmitted from the transmitter 4. The pulse width at the output terminal of the pulse generator in the transmitter 4 is about 0.7 nsec. Also, when the radio wave is output from the transmission antenna 2, the band width is limited by the transmission antenna 2 so that the pulse width slightly changes, but the emitted electromagnetic wave has a waveform substantially as shown in FIG.
[0040]
In the present embodiment, as shown in FIG. 16, the surface of the test object serving as the search object 91 is moved in the X direction and the Y direction orthogonal to each other by using the search device including the electromagnetic wave transmitting / receiving device 1 having the transmission antenna 2 and the reception antenna 4. , And run continuously at fixed intervals Δx and Δy to search. An electromagnetic wave is injected into the search object 91 from the transmitting antenna 2 at intervals of 5 mm. As shown in FIG. 17, the electromagnetic wave is reflected at a place having different electrical properties from the surrounding medium, such as a buried pipe. Therefore, the distance to the buried object is measured from the propagation time. The distance to the buried object is obtained by the following equation.
[0041]
(Equation 1)
V = C / {(ε)} [m / sec]
(Equation 2)
D = (VT) / 2 [m]
[0042]
Here, V is the speed of the electromagnetic wave in concrete, C is the speed of the electromagnetic wave in vacuum, ε is the relative permittivity of concrete, D is the distance to the buried object, and T is the electromagnetic wave speed. Round trip propagation time.
[0043]
In addition, since this exploration device does not have perfect directivity and receives a wide range of reflected waves, even if there is no buried object directly below the search point, if it is present in the vicinity, it will receive the reflected wave . However, since the propagation time is different from the case where the buried object exists immediately below, as is well known, the reflected wave group of the buried object has a hyperbolic shape as shown in FIG. As shown in FIG. 19A, the received wave is composed of a reflection component of the surface, a reflection component of the buried object, and a reflection component (a noise waveform) of the buried object in a wide area. The search result is represented by a grayscale image of 256 gradations as shown in FIGS. 19B and 19C based on the amplitude of the received wave.
[0044]
Next, the characteristics of the received waveform will be described. The reflected wave group of the buried object basically has a characteristic shape of a hyperbola. However, this shape is when the burial depth is shallow or when a single buried object is searched, and when the burial depth is deep and there are multiple buried objects, the influence of the mutual interference of the reflected waves is greatly affected. However, the reflected wave group does not always form a hyperbolic pattern.
[0045]
In the present embodiment, attention is paid to the amplitude of the received wave. FIG. 20 and FIG. 21 show the received waveform and the search result image when the buried object exists. The waveform shown in FIG. 20B is a waveform observed on the La line in FIG. 20A, and is an example in which a reflected wave of a basic buried object is received. As shown in FIG. 20, the reflected wave of the buried object clearly has a large amplitude peak, so that the buried position can be easily determined. On the other hand, the waveform shown in FIG. 21B is a waveform observed on line Lb in FIG. In this waveform, mutual interference occurs between the reflected wave of the buried object and the reflected wave of the surface of the test object, and the received waveform is greatly distorted. This distortion has a great effect on the reflected wave of a buried object having a small depth, and suppresses an increase in the amplitude as shown in FIG. In this case, it is difficult to find a clear difference from the noise waveform, and as a result, it is difficult to determine the embedding position. However, the interference wave suppresses the reflected wave mainly due to the amplitude rise, and the amplitude fall is not suppressed as much as the amplitude rise, and has a characteristic that the interference wave falls to a value sufficiently smaller than the noise waveform. Since the portion where the amplitude decreases becomes a dark portion on the image, the embedding position can be estimated by limiting the target to the low density region. From the above features, in the present embodiment, the embedding position is estimated by paying attention to the hyperbolic pattern of the low concentration area and the reflected wave group.
[0046]
Next, the embedded object exploration image processing (main routine) will be described with reference to FIG. In FIG. 3, first, in step S1, the reception and storage processing of the reflected wave signal data is executed. Here, the data of the reflected wave signal transmitted from the electromagnetic wave transmission / reception device 1 is received via the communication interface 61 and stored in the reception memory 26. Next, in step S2, an initial value (predetermined value) of an estimation processing parameter (corresponding to the measurement parameter in the first embodiment) is generated and set, and in step S3, reflection during scanning in the X direction is performed. A burying position candidate estimation process (FIG. 4) based on the wave signal data is executed. Here, among the data of the reflected wave signal in the reception memory 26, the hyperbolic pattern is focused on based on the reflected wave signal data at the time of scanning in the X direction, and image data of the XZ section of the embedding position candidate (hereinafter, image of XZ slice) ) Is stored in the image memory 23. Further, in step S4, an embedding position candidate estimation process (FIG. 5) based on the reflected wave signal data at the time of scanning in the Y direction is executed. Here, of the reflected wave signal data in the reception memory 26, the hyperbolic pattern is focused on based on the reflected wave signal data at the time of scanning in the Y direction, and image data of the YZ section of the embedding position candidate (hereinafter, image of YZ slice) ) Is stored in the image memory 23. In the estimation processing in steps S3 and S4, the original image is quantized into three regions in order to extract a low density region of the image, labeling is performed on the quantized region, and the hyperbolic pattern of the reflected wave group and the reflected wave The position of the buried object is estimated using the characteristics of the amplitude.
[0047]
Then, in step S5, addresses are virtually allocated and stored in the image memory 23 so that the data of the two estimation processing results are combined and arranged in a three-dimensional space. Further, in step S6, after executing a connection search process (FIGS. 10 and 11) of connecting and searching candidate points of the embedding position to generate connection data, in step S7, a predetermined fuzzy rule is generated based on the connection data. To perform a buried object extraction process (FIG. 12) for determining whether or not the object is a buried object such as a buried pipe extending continuously in a predetermined direction. Next, in step S8, a process (FIG. 13) of adaptively controlling and setting the parameters for the estimation process using the genetic algorithm based on the result of the extraction process is executed. In step S9, it is determined whether or not a predetermined termination condition is satisfied. If determined to be NO, the process returns to step S3 to repeat the above processing, while to YES, the process proceeds to step S10. Here, as the termination condition, for example,
(A) Counting the number of repetitions in which the processes from step S3 to step S9 are executed, and exceeding a predetermined threshold value of the number of repetitions (the pre-threshold which becomes a convergence state as described later with reference to FIG. 39) Value.) Or
(B) The difference between the estimation processing parameters before and after the adaptive control processing in step S8 is equal to or smaller than a predetermined threshold value (the convergence state in which the difference between the estimation processing parameters before and after the adaptive control processing hardly changes) When
Can be set to
[0048]
Lastly, in the embedded object image output process in step S10, the image of the embedded object (the image showing the internal configuration of the structure) in the exploration object determined to be the embedded object in the embedded object extraction process is a three-dimensional image. Is displayed on the CRT display 43 or printed out by the printer 44 and output, and the embedded object search image processing ends.
[0049]
Further, the extraction processing of the candidate density performed in steps S3 and S4 will be described in detail. FIG. 22 shows the density distribution of the search result image. First, in order to extract a low-density region corresponding to a portion where the amplitude decreases, threshold processing is performed using the average value Th of the amplitude. Next, a known histogram equalization method is applied to the density region equal to or smaller than the threshold value Th to quantize the image into two gradations. FIG. 23B shows the result of applying this processing to the original image shown in FIG. As a result, three regions are obtained: a low-density region A in which a buried object is likely to exist, a mixed region B of noise and a buried object, and a region C other than A and B. The surface reflection component is removed in advance based on the information on the depth.
[0050]
Among the extracted regions, there is a low concentration region having a high probability of the presence of a buried object at the center as in a region RRa of FIG. 24, and a region where a mixed region of noise and the buried object exists so as to surround this region. The probability of being an image of a buried object is very high. However, it is considered that a region such as the region RRb of FIG. 24 which is composed of only the mixed region of the noise and the buried object has a low probability of being an image of the buried object. Furthermore, if this region does not have continuity between the preceding and following slices, it can be said that the region is not an image of the buried object. On the basis of the above knowledge, a region which is constituted only by a mixed region of noise and a buried object and has no continuity is removed. As a result, only a region having a high probability of being an image of a buried object remains as a candidate region.
[0051]
Next, the embedding position estimation processing performed in the estimation processing of steps S3 and S4 will be described in detail. Labeling is performed on the buried object candidate area obtained by the previous extraction processing. In the case of the extraction result shown in FIG. 23B, it can be divided into seven regions (N1 to N7) as shown in FIG. Next, the embedding position is estimated with reference to the original image data for each area. The reflected wave of the buried object has a larger amplitude if the distance from the search point to the buried object is shorter, that is, if the buried object exists immediately below the search point. In the present embodiment, since the focus is on the low-density area, the minimum point of each candidate area can be obtained, and this point can be used as the estimated position of the buried object.
[0052]
Assuming that all the candidate regions are not affected by the mutual interference of the reflected waves, basically one estimated position can be given per region. However, there is also a region having three minimum points, such as a region N4 shown in FIG. Since there are two buried pipes in this area and the space between them is close to each other, the reflected waves reinforce each other due to mutual interference, and an image of the same density area as the image of the buried object is generated in a place where nothing is originally present. As a result, there are three minimum points. At this stage, it is difficult to determine which part is the strengthening part, so all three parts are listed as candidates for burying positions. FIG. 25B shows the estimation result of the buried position by the minimum point search.
[0053]
The influence of the mutual interference of the reflected waves can also be cited. In the buried object candidate area shown in FIG. 27, since there are a plurality of buried objects near the surface, the influence of the mutual interference attenuates the reflected wave of the buried object, making it difficult to search by a minimum point. In such a region, the embedding position can be estimated using a hyperbolic pattern which is a feature of the reflected wave group. First, the valley line of the candidate area is extracted with reference to the density of the original image. Next, when the reflected wave group forms a hyperbolic pattern, the buried object corresponds to the position of the vertex of the convex hyperbola. Therefore, the vertex of the convex hyperbola of the valley is extracted and used as the estimated position of the buried object.
[0054]
In consideration of the above factors, the embedding position is estimated using both the minimum point and the hyperbolic pattern. In some cases, an estimated position based on the minimum point and an estimated position based on the hyperbolic pattern are obtained in one region. In this case, priority is given to the position estimated by the hyperbolic pattern.
[0055]
Next, the process of connecting the estimated positions performed in step S6 will be described in detail. The estimated position of the buried pipe extracted by the above estimation processing is a point on the XZ slice or the YZ slice, and corresponds to a point in space in the search area. For this reason, estimation results considered to be estimating the same pipe are connected. First, the extracted results of estimating all the embedded positions in the X and Y directions are arranged in a three-dimensional space. Next, as shown in FIG. 28, a search is performed within a sphere within a predetermined radius from a candidate point to be processed based on each estimation result, and connected to a point at which the distance between the estimated positions is the shortest. At this time, the connection is performed with priority given to the estimation result of the slice in the same direction. That is, in the case of image data of an XZ slice, the estimation result in the X direction is connected with priority, and only when there is no candidate point, the estimation result in the Y direction, which is another direction, is connected. In the case of image data of YZ slices, the estimation result in the Y direction is connected with priority, and only when there is no candidate point, the estimation result in the X direction, which is another direction, is connected.
[0056]
Further, the process of extracting a buried pipe performed in step S7 will be described in detail. In this extraction process, the connection result of the estimated position is evaluated based on the knowledge of the shape, continuity and depth of the buried pipe, and only the buried pipe is extracted, thereby extracting the buried pipe position with higher accuracy. . Since the buried pipes targeted in the present embodiment are mainly electric conduits, gas pipes, water pipes, etc., the buried pipes are not interrupted in the middle of the exploration area, and are connected from end to end in the exploration area. It is assumed that Further, the concrete structure of the embodiment has a cover thickness of several cm, which is a layer of concrete only, from the surface of the concrete to the internal piping, and there is no buried pipe between the cover thicknesses. With the above knowledge, the following rules can be created. Fuzzy inference is performed according to this rule, and buried pipes are extracted. FIG. 29 shows a membership function.
[0057]
(Equation 3)
Rule 1:
"As a result of connecting the estimated IF positions, both ends are near the end of the exploration area,
The depth of the buried pipe is not located between the cover thickness.
The probability of being a THEN buried pipe is high (grade A). “
(Equation 4)
Rule 2:
“As a result of connecting the estimated IF positions, both ends are far from the end of the
The depth of the buried pipe is located between the cover thickness.
The probability of being a THEN buried pipe is low (grade B). “
[0058]
Here, the farthest from the search area is the shortest distance from both end points of the connection result to the outer periphery of the search area, and the depth is an average value of the depth of the connection result. The degree of distance from the outer periphery of the estimated position connection result is represented by G_P, Degree of depth G_DThe degree of being a buried pipe is grade @ A, and the degree of being not a buried pipe is grade @ B. grade @ A and grade @ B are obtained by the following equations.
[0059]
(Equation 5)
grade @ A
= W₁G_PNear+ W₁G_PNear+ W₂G_DMiddle
(Equation 6)
grade @ B
= W₁G_PFar+ W₁G_PFar+ W₂G_DDeep+ W₂G_DShallow
[0060]
Where W₁, W₂Are weighting factors, each set to 1 in the preferred embodiment. Then, a connection result of grade @ A> grade @ B is determined to be a buried pipe which is a continuously extending buried object. In the present embodiment, for example, it is assumed that the blown thickness is 3 cm, the thickness of the exploration object in the burying depth direction (Z direction) is 100 cm, and the distance from the outer periphery is “close” when it is 3 cm and “far” when it is 6 cm. At this time, the parameters L1, L2, L3, L4, and L5 in FIG. 29 are as follows.
L1 = 3 cm.
L2 = 6 cm.
L3 = 3 cm.
L4 = 94 cm.
L5 = 97 cm.
[0061]
Further, the processing of each subroutine of FIG. 3 except for step S8 will be described in detail below.
[0062]
FIG. 4 is a flowchart showing an embedding position candidate estimating process (step S3) based on reflected wave signal data at the time of scanning in the X direction, which is a subroutine of FIG. In FIG. 4, first, in step S11, the reflected wave signal data at the time of scanning in the X direction is converted into density data of 256 gradations according to the magnitude of the amplitude, and XZ slice image data having the density data is generated. Next, in step S12, as shown in FIG. 22, the image data of the XZ slice is quantized into three regions in the grayscale direction, and in step S13, the image data of the low-density region is extracted and used as the image data of the embedding position candidate. Then, in step S14, as shown in FIG. 24, the image data of the region having no continuity is removed from the image data of the XZ slice of the embedding position candidate based on the front-back relationship in the Y direction. Further, in step S15, as shown in FIG. 25 and FIG. 26, a process of estimating the image data of the embedding position candidate based on the XZ slice image data of the embedding position candidate after removal by searching for a minimum point of shading (FIG. Execute 6). Finally, in step S16, the image data of the estimated XZ slice of the embedding position candidate is extracted and stored in the image memory 23, and then the process returns to the original main routine.
[0063]
FIG. 5 is a flowchart showing the embedding position candidate estimating process (step S4) based on the reflected wave signal data at the time of scanning in the Y direction, which is a subroutine of FIG.
In FIG. 5, first, in step S21, the reflected wave signal data at the time of scanning in the Y direction is converted into density data of 256 gradations according to the magnitude of the amplitude, and YZ slice image data having the density data is generated. Next, in step S22, as shown in FIG. 22, the image data of the YZ slice is quantized into three regions in the grayscale direction, and in step S23, the image data of the low-density region is extracted and used as the image data of the embedding position candidate. Then, in step S24, as shown in FIG. 24, the image data of the area having no continuity is removed from the image data of the YZ slice of the embedding position candidate based on the X-direction context. Further, in step S25, as shown in FIG. 25 and FIG. 26, a process of estimating the image data of the embedding position candidate by, for example, searching for a minimum point of shading based on the image data of the YZ slice of the embedding position candidate after the removal, The processing is performed in the same manner as the processing in FIG. Finally, in step S26, the image data of the estimated YZ slice of the embedding position candidate is extracted and stored in the image memory 23, and then the process returns to the original main routine.
[0064]
Next, an embedding position estimation method in the processing of estimating the image data of the embedding position candidate in step S15 of FIG. 4 and step S25 of FIG. 5 will be described below.
[0065]
First, the knowledge about the density will be described below. The buried pipe appears as a local density change as shown in FIG. 30A in the grayscale image data obtained up to step S14 in FIG. 4 or step S24 in FIG. For this reason, the search for the density minimum point is effective as a means for estimating the buried position. However, since a feature similar to this density change is also generated by interference of a reflected wave as shown in FIG. 30B, it is difficult to estimate a buried position only by searching for a minimum point. Therefore, the method according to the present embodiment focuses on the density change from the minimum point to its surroundings. As shown in FIG. 30 (a) and FIG. 30 (b), the concentration change around the minimum point is obtuse near the buried pipe, whereas it is acute in the interference region. At this time, the angle of the density change is θ_azThen, the following fuzzy If-Then rule can be constructed.
[0066]
(Equation 7)
If 角度 angle of density change θ_azHas an acute angle (Acute), and the burial of the Then buried pipe is low (Low).
(Equation 8)
If 角度 angle of density change θ_azHas an obtuse angle (Obtuse), and the degree of affiliation of the The buried pipe is high (High).
(Equation 9)
If 角度 angle of density change θ_azHas a flat angle (Straight), and the buried pipe of the Then belongs to a low degree (Low).
[0067]
Then, the degree μ belonging to the buried pipe based on the knowledge of the concentration μ_gIIs determined by the well-known Min-Max centroid method from the membership function shown in FIG. In the input membership function of FIG. 31A, the following values are the parameters for estimation processing according to the present embodiment.
(A) Flat angles T1 and T5 of the density change when the grade of the membership function of “obtuse angle” becomes 0.
(B) The flat angle T2 of the density change at the boundary when the grade of the “acute angle” membership function decreases from 1.
(C) The flat angle T3 of the density change when the grade of the membership function of “acute angle” and “flat angle” becomes 0.
(D) The flat angle T4 of the density change at the boundary when the grade of the “flat angle” membership function becomes 1.
[0068]
That is, in the example of FIG. 31, the average angle θ of the density conversion_azIs T_in, Intersects each function line of the membership function of “obtuse angle” and “flat angle”, extends these intersections horizontally to the membership function of the output, where the straight line extended from the intersection of “obtuse angle” Area A below grade that intersects the function curve of the "high" output power membership function_HAnd the area A below the grade where a straight line extending from the intersection of the "flat angle" intersects the function curve of the membership function of the "low" output_LArea centroid μ in the area of the union with_gIAnd the output value μ of the degree of the buried pipe_gIAnd
[0069]
Next, knowledge about the hyperbolic shape will be described below. The buried pipe appears as a hyperbolic shape shown in FIG. 32A in the grayscale image data obtained up to step S14 in FIG. 4 or step S24 in FIG. By searching for this vertex, the embedding position can be estimated. Here, the hyperbolic shape is obtained as shown in FIG. 32B by a binarization process in which the minimum point of the received wave is “0” and the other points are “1”. The degree of coincidence α between the hyperbola obtained from the image data and the theoretical hyperbola (see FIG. 18 or FIG. 32 (c)) is obtained, and belongs to a buried pipe based on knowledge of the hyperbolic shape according to the fuzzy If-Then rule shown below. Degree μ_gHPAsk for. Note that, for the degree of coincidence α between the hyperbola obtained from the image data and the theoretical hyperbola, for example, the maximum point of the theoretical hyperbola corresponds to the maximum point of the hyperbola obtained from the image data, and the image data is finely divided into a mesh shape. The number of segments that match in the segmented mesh can be calculated as the degree of coincidence.
[0070]
(Equation 10)
If coincidence α is large (Many), Then buried pipe is highly affiliated (High),
[Equation 11]
If coincidence α is small (Few), and the membership of the Then buried pipe is low (Low).
[0071]
Then, the degree μ belonging to the buried pipe based on the knowledge of the hyperbolic shape μ_gHPIs obtained by the well-known Min-Max centroid method from the membership function shown in FIG. In the input membership function of FIG. 33A, the following values are estimation processing parameters according to the present embodiment.
(A) The degree of coincidence A1 when the grade of the membership function of “many” becomes 0. (B) The degree of coincidence A2 of the boundary when the grade of the membership function of “low” decreases from 1;
(C) The degree of coincidence A3 when the grade of the membership function “less” becomes 0.
(D) The flat angle A4 of the density change at the boundary when the grade of the membership function of “many” becomes 1;
[0072]
That is, in the example of FIG._in, Intersect each of the function lines of the “more” and “less” membership functions and extend these intersections horizontally to the output membership function, where the straight line extending from the “more” intersection is Area A below grade that intersects the function curve of the "high" output power membership function_HAnd the area A below the grade where the straight line extending from the intersection of "low" intersects the function curve of the membership function of "low" in output._L′ And the area centroid μ in the area of the union with_gHPAnd the output value μ of the degree of the buried pipe_gHPAnd
[0073]
Further, integration of knowledge of density and knowledge of hyperbolic shape will be described below. An expert in burial exploration estimates the buried pipe by weighting the above two knowledge according to the depth. At a position where the depth is shallow, knowledge of the hyperbolic shape is prioritized, and at a position where the depth is deep, knowledge of the density is prioritized. Therefore, in the method according to the present embodiment, the weighting coefficient of each affiliation is changed according to the depth as shown in FIG. Where w_iIs the membership degree μ_gI, And w_hIs the membership degree μ_gHPWeight. FIG. 34 shows a function of the weighting coefficient with respect to the depth._iThe function of the trapezoidal shape on the right is the weighting coefficient w_hIs a function of Here, in the two trapezoids, the length (D6-D1) of the base of the trapezoid and the length Da of the upper side are set to be equal, and in the function of the weighting coefficients, D1, D2, D4, Da, If the five parameter values of D6 can be determined, the function is defined. The leftmost portion in FIG. 34 is a so-called “fogging” portion located at a shallow portion.
[0074]
Further, the degree of affiliation μ obtained above_gI, Μ_gHPAnd weighting factor w_i, W_hCan be combined as follows to obtain the degree μ of the buried pipe based on all the knowledge._TDAsk for. And μ_TDImage data satisfying> 0.5 is set as image data of a buried pipe candidate.
[0075]
(Equation 12)
μ_TD= (W_i× μ_gI+ W_h× μ_gHP) / 2
[0076]
Further, the processing of each subroutine of FIGS. 6 to 9 for executing the processing using the knowledge of the density, the knowledge of the hyperbolic shape, and the method of integrating them will be described below.
[0077]
FIG. 6 is a flowchart showing a process (step S15) of estimating the embedment position candidate by searching for a minimum point of light and shade based on the image data of the XZ slice of the embedment position candidate, which is a subroutine of FIG. In FIG. 6, first, a process (FIG. 7) of estimating an embedding position candidate by searching for a minimum point of light and shade based on the image data of the XZ slice of the embedding position candidate after the removal in step S71 is executed, and after the removal in step S72. In the XZ slice image data of the embedding position candidate, a process of extracting the valley line with reference to the density and estimating the embedding position candidate by looking at the convex hyperbolic pattern (FIG. 8) is executed. Further, in step S73, the process of estimating the two embedding position candidates is integrated by weighting (FIG. 9), and the process returns to the original routine.
[0078]
Step S15 in FIG. 6 is a process of estimating the image data of the embedding position candidate based on the image data of the XZ slice of the embedding position candidate after the removal, while step S25 in FIG. 5 is processing of estimating the embedding position candidate after the removal. This is a process of estimating the image data of the embedding position candidate based on the image data of the XZ slice, and is executed similarly to the process of FIG.
[0079]
FIG. 7 is a flowchart showing a process (step S71) of estimating a burying position candidate by searching for a minimum point of light and shade, which is a subroutine of FIG. In FIG. 7, first, based on the image data of the XZ slice of the embedding position candidate after removal in step S81, the angle θ of the density change from the minimum point of shading to its surroundings_azAsk for. Next, in step S82, the average angle θ of the density change_azOf candidate burial position based on_gIIs obtained by the Min-Max centroid method using the membership function shown in FIG. 31, and the process returns to the original routine.
[0080]
FIG. 8 is a flowchart showing a process (step S72) of estimating an embedding position candidate by looking at a hyperbolic pattern which is a subroutine of FIG. In FIG. 8, first, in the image data of the XZ slice of the embedding position candidate after removal in step S83, the degree of coincidence α between the hyperbola obtained by referring to the density and the theoretical hyperbola is obtained. Next, in step S84, the degree μ of the embedding position candidate based on the degree of coincidence α between the hyperbola obtained by referring to the density and the theoretical hyperbola_gHPIs obtained by the Min-Max centroid method using the membership function shown in FIG. 33, and the process returns to the original routine.
[0081]
FIG. 9 is a flowchart showing a process (step S73) of integrating the processes of estimating the two embedding position candidates, which are subroutines of FIG. 6, by weighting. In FIG. 9, in step S85, the degree μ of the burial position candidate by the minimum point search_gIWeighting coefficient w_iAnd the degree of embedding position candidate μ by hyperbolic pattern_gHPWeighting coefficient w_hIs obtained by changing according to the depth as shown in FIG. Next, in step S86, the obtained degree and the weighting coefficient are linearly combined using Equation 12, thereby obtaining the degree μ of the buried pipe based on all knowledge._TDAsk for. Further, in step S87, μ_TDThe image data satisfying> 0.5 is extracted as the image data of the burial pipe candidate, and the process returns to the original routine.
[0082]
FIGS. 10 and 11 are flowcharts showing the connection search processing (step S5) which is a subroutine of FIG.
[0083]
In step S31 of FIG. 10, first, image data of a candidate point is searched for on one XY plane and stored in the processing memory 25 which is a temporary memory. Next, in step S32, the image data of one candidate point is selected, and in step S33, the candidate point is searched within a range sphere centered on the selected candidate point. Then, it is determined whether or not the candidate point selected in step S34 is XZ slice image data. If YES, the process proceeds to step S35, while if NO, the process proceeds to step S37. In step S35, it is determined whether or not a candidate point has been found in the image data of another XZ slice. If YES, the process proceeds to step S40, and if NO, the process proceeds to step S36. In step S36, it is determined whether a candidate point has been found in image data of another YZ slice. If YES, the process proceeds to step S40, and if NO, the process proceeds to step S42. Further, in step S37, it is determined whether or not a candidate point has been found in the image data of another YZ slice. If YES, the process proceeds to step S40, while if NO, the process proceeds to step S38. In step S38, it is determined whether or not a candidate point has been found in the image data of another XZ slice. If YES, the process proceeds to step S40, while if NO, the process proceeds to step S42.
[0084]
In step S40, it is determined that the selected candidate point and the searched candidate point are connected, and the selected candidate point and the shortest candidate point among the searched candidate points are connected and temporarily stored as connected data. After being stored in the processing memory 25, which is a memory, and the candidate points connected in step S41 are set as the selected candidate points, the process returns to step S33 to perform further connection processing. Here, the connected data is data indicating that the image data of one candidate point and the image data of another candidate point are connected, and the connection data of the arrangement address of the three-dimensional image of the two candidate points. Expressed in pairs.
[0085]
It is determined whether there is image data of an unprocessed candidate point other than the one selected in step S42. If YES, the process proceeds to step S43, where image data of another candidate point is selected, and another candidate point is selected. The process returns to step S33 to search for a certain connection state. On the other hand, if NO in step S42, the process proceeds to step S44 in FIG.
[0086]
In step S44 of FIG. 11, it is determined whether or not there is an unprocessed XY plane. If YES, the process proceeds to step S45, while if NO, the process proceeds to step S46. In step S45, after storing the image data of the candidate point on another XY plane in the processing memory 25 which is a temporary memory searched, the process returns to step S32 of FIG. 10 to perform processing on the image data of another XY plane. . On the other hand, an average interval is calculated for candidate lines of buried objects in the consolidated data collected in step S46, candidate lines within a predetermined interval are determined to be the same buried object, and a plurality of candidate lines determined to be the same buried object are determined. The connection data of the candidate lines other than one of the candidate lines is removed from the processing memory 25. Then, the remaining connection data is stored in the processing memory 25 as connection result data in step S47, and the process returns to the original main routine.
[0087]
In FIGS. 10 and 11, the selection of the XY plane in steps S31 and S45 is preferably performed sequentially in the direction of the burial depth from the search plane 92, and the selection of a candidate point on one XY plane is determined by selecting the plane It is preferable to search from a candidate point located coaxially from the outer periphery of, and process while searching for a candidate point located coaxially inward.
[0088]
FIG. 12 is a flowchart showing the embedded object extraction process (step S6) which is a subroutine of FIG.
[0089]
In FIG. 12, first, in step S51, one connection result data is selected from the connection result data in the processing memory 25, and two embedding position candidates located at both ends of the connection result data selected in step S52 are determined from the outer periphery. An average value of the distance and the degree of search for a plurality of candidate embedding positions of the selected connection result data is obtained. Next, in step S53, each grade G for two distances from the outer circumference is determined using the membership function of FIG._pNearAnd G_pFarIs calculated, and in step S54 each grade G with respect to the average value of the search is determined using the membership function of FIG._pShallow, G_pMiddle, G_pDeepIs calculated. Then, in step S55, the grading degree A of the buried pipe is calculated using equation 5, and the grading degree B not belonging to the buried pipe is calculated using equation 6, and it is determined in step S56 whether grade A> grade B. If YES, the process proceeds to step S57, while if NO, the process proceeds to step S58. The connection result data selected in step S57 is extracted as a buried pipe, stored in the image memory 23, and proceeds to step S59. On the other hand, it is determined that the connection result data selected in step S58 is not a buried pipe, and the process proceeds to step S59 without storing it in the image memory 23. Further, it is determined in step S59 whether or not there is unprocessed connection result data. If YES, the process proceeds to step S60 to select another connection result data from the connection result data in the processing memory 25. It returns to step S52. On the other hand, when NO is determined in the step S59, the buried object extraction processing is ended and the process returns to the main routine.
[0090]
FIG. 13 is a flowchart showing a process (step S8) of adaptively setting parameters for estimation processing using a genetic algorithm, which is a subroutine of FIG. In FIG. 13, first, an evaluation process is executed in step S91, a selection process is executed in step S92, a crossover process is executed in step S93, a mutation process is executed in step S94, and the process returns to the original main routine. . The processes in steps S91 to S94 will be described in detail below.
[0091]
As described above, the following 14 parameters for estimation processing that need to be optimized by adaptive control are provided.
(A) Five estimation processing parameters T1 to T5 for defining the membership function of the input of the knowledge of density in FIG.
(B) Four estimation processing parameters A1 to A4 that define the membership function of the input of the knowledge of the hyperbolic shape in FIG.
(C) Five estimation processing parameters D1, D2, D4, D6, and Da that define the function of the weighting coefficient in FIG.
[0092]
In the present embodiment, an individual of a gene in the genetic algorithm is represented as an array in which 1 bit is 0,1. A different variation range of each estimation processing parameter is expressed by 8 bits, and for example, 10 individuals of 112 bits in total are generated by 14 estimation processing parameters, and an initial individual population is created. The initial value of the estimation processing parameter set in step S2 is used. Here, in the present embodiment, in order to converge the optimal solution faster, the conventional optimal value under a single environment is assigned as each initial individual population. Note that when the environment greatly changes, such as a change in the specifications of the radar device or a change in the data conversion method, it is preferable to randomly generate the initial individual population.
[0093]
When the initial population is generated, a fitness evaluation process required for the selection, crossover, and mutation operators is performed (step S91). First, it is assumed that input data used in the present system is as follows.
<Assumption 1> The input data includes information on all buried pipes.
<Assumption 2> Nothing other than the buried pipe exists in the input data.
That is, it is assumed that all the buried pipes have been searched for in the input data, and there is nothing other than the buried pipes. From these assumptions, the extraction result of this system is derived as follows.
<Result 1> In the extraction result, all the buried pipes exist.
<Result 2> The extraction result does not include anything other than the buried pipe.
Further, in the present system, since the relationship between each parameter and the number of buried pipes extracted is non-linear, the fitness g is defined as follows.
[0094]
(Equation 13)

[0095]
Here, n is the number of buried pipes extracted, and l_iIs the length of the i-th buried pipe. Since the objects extracted from <Result 2> are all buried pipes, the sum of their lengths (fitness g) is the extraction degree of buried pipes. Then, when <Result 1> is satisfied, the fitness g indicates the maximum value. For example, when no buried pipe is extracted as shown in FIG. 35 (a), the fitness g becomes low, and when the pipe is accurately extracted as shown in FIG. 35 (b), the fitness g becomes high. Thus, the larger the total length of the buried pipes, the more accurately the buried pipes can be extracted, and the higher the fitness g becomes.
[0096]
In the selection processing (step S92) according to the present embodiment, a known roulette selection method is used. The roulette wheel selection method is a method in which the selection probability is changed based on the fitness of each individual, and the roulette is selected so as to rotate according to the probability. Individuals with higher fitness are more likely to be selected. The fitness of an individual i is g_iAnd the number of individuals is M, the selection probability P as shown in the following equation_iMake your selection with.
[0097]
[Equation 14]

[0098]
FIG. 36 shows an example of selection processing by the roulette selection method used in the present embodiment. Here, for example, when ten individuals having different parameters are given as in the present embodiment, each fitness g is calculated using the evaluation function shown in Expression 12 above._iIs calculated, and from the result, each selection probability P_iAsk for. This selection probability P_iIs arranged in roulette as shown in FIG. 36 (d), the selection probability P_iIs larger, the greater the occupancy, the higher the probability of being selected most.
[0099]
Next, in the crossover process (step S93), a so-called one-point crossover method is used. The one-point crossover method uses the crossover probability p as shown in FIGS._c, Two crossing points are randomly selected at the bit boundary of 1 to 111, and the subsequent gene sequence is partially exchanged at the point.
[0100]
Further, in the mutation processing (step S94), as shown in FIG._mThe bit to be mutated is determined from bits 1 to 112, and the bit is inverted.
[0101]
FIG. 39 is a graph showing the transition of the degree of conformity to the number of generations T in the embodiment of the embedded object exploration system of FIG. In the present embodiment, the number of generations (the number of repetitions) is changed by the loop shown in FIG. 3, and it is preferable to update the parameters until the last generation (the number of repetitions) T = 1000. Alternatively, the updating of the parameter may be ended when the rate of change becomes smaller due to the change in the fitness of the number of generations as shown in FIG.
[0102]
As described above, according to the present embodiment, attention is paid to the hyperbolic pattern which is a feature of the reflected wave group from the shallow portion and the single buried object, and the feature that the amplitude drop of the reflected wave is hardly affected by the interference wave. The position of the buried object was estimated, and the parameters for the estimation processing were adaptively controlled using a genetic algorithm. Based on the results, a three-dimensional image of the internal structure was generated and output. As a result, a three-dimensional image of the inside of the structure can be generated with high accuracy, and as a result, information on the inside of the structure necessary for construction can be obtained. Therefore, it is possible to execute the buried object search processing while adaptively controlling the measurement parameters used when executing the buried object search processing to the optimum values, and to easily and accurately search for the buried object in various kinds of objects to be searched. Anyone can easily confirm the position of the buried object visually.
[0103]
In the above-described second embodiment, the buried object extraction processing (step S7) is performed after the connection exploration processing (step S6) is performed. However, the present invention is not limited to this, and the latter buried object extraction processing is performed. The processing need not be performed.
[0104]
In the second embodiment described above, in step S4, the image memory is virtually assigned by assigning addresses so that the data of the estimation processing results of the two cross sections of the XZ slice and the YZ slice are combined and arranged in a three-dimensional space. 23, the present invention is not limited to this, and data of the estimation processing result of at least one cross section of the XZ slice and the YZ slice is virtually combined and arranged in a three-dimensional space. An address may be assigned and stored in the image memory 23. That is, the process of searching for an embedded object may be performed based on only the cross-sectional image of one slice.
[0105]
【Example】
FIGS. 40 to 42 are diagrams showing search results in the first to third embodiments of the buried object detection system of FIG. 2, respectively. FIG. It is a figure which shows a result, (b) is a figure which shows the search result image before applying the method concerning this embodiment, and (c) shows the search result image after applying the method concerning this embodiment. FIG. Note that broken lines (squares) in the results shown in (b) and (c) of FIGS. 40 to 42 indicate portions corresponding to X-ray images.
[0106]
As is clear from FIG. 40 to FIG. 42, in the conventional method, at the time of three-dimensional display, since the fourteen estimation processing parameters of the system were fixed in advance, the buried pipe indicated by the broken line was not detected at all. However, as shown by the oval dashed lines in FIGS. 40 to 42, the internal parameters are automatically optimized by applying the method according to the present embodiment, so that the embedded parameters not detected by the conventional method are embedded. Tubes could be detected.
[0107]
<Modification>
In the above embodiment, when the program data of the embedded object exploration image processing of FIG. 3 is stored in the CD-ROM 45a and executed, the program data is loaded into the program memory 24 and executed, but the present invention is not limited to this. , CD-R, CD-RW, DVD, MO, etc., may be stored in various recording media, such as recording media of optical disks or magneto-optical disks, or recording media of magnetic disks, such as floppy disks. These recording media are computer-readable recording media. Further, the program data of the embedded object exploration image processing of FIG. 3 may be stored in the program memory 24 in advance and the image processing may be executed.
[0108]
In the above embodiment, the buried object continuously extending in the predetermined length direction in the search object, radiating the transmission wave of the electromagnetic wave from the surface of the search object, receiving the reflected wave of the transmission wave, Although the method and apparatus for searching for a buried object for searching based on the reflected wave data are described, the present invention is not limited to this, and the search processing for the buried object is similarly performed using ultrasonic waves instead of electromagnetic waves. May be.
[0109]
In the embodiment described above, in the adaptive control processing of the parameters for the position estimation processing using the genetic algorithm, an example is shown for each processing of evaluation, selection, crossover, mutation, and determination. It is not limited.
[0110]
【The invention's effect】
As described in detail above, according to the adaptive control type measurement processing method or apparatus according to the present invention, the measured input data is received, the initial value of the measurement parameter used at the time of measurement is set, and the received input data is set based on the received input data. The measurement processing result is obtained by executing a predetermined measurement processing using the measurement parameter set in the above, and the degree of conformity of the measurement parameter is high using a genetic algorithm based on the obtained measurement processing result. The measurement parameters are adaptively controlled and set so as to determine whether or not a predetermined end condition is satisfied after the setting process. If the end condition is not satisfied, the measurement process and the setting process are repeated. When the termination condition is satisfied, the measurement processing result in the last measurement processing is output as output data. Therefore, it is possible to execute the measurement process while adaptively controlling the measurement parameters used when the above-described measurement process is performed to the optimum value, and it is possible to execute the measurement process with higher precision and accuracy compared to the related art. .
[0111]
Further, according to the method or apparatus for burying object exploration according to the present invention, the buried object continuously extending in the predetermined length direction in the exploration object, the electromagnetic wave or the transmission wave of the ultrasonic wave from the surface of the exploration object. Radiating, receiving the reflected wave of the transmitted wave, in a buried object exploration processing method or apparatus for searching based on the reflected wave data, based on the received reflected wave data, for the buried object exploration Extracting data of a predetermined feature parameter, and, based on the extracted feature parameter data, using a genetic algorithm, adaptively controlling the position estimation processing parameter such that the degree of conformity of the position estimation processing parameter is increased. Meanwhile, the position of the buried object is estimated using the knowledge that the buried object extends continuously, and the image of the buried object is converted into a three-dimensional image based on the estimated position of the buried object. It generates and outputs in the form. Therefore, the embedded object exploration process can be executed while adaptively controlling the measurement parameters used when executing the embedded object exploration process to the optimum value, and the embedded object can be easily compared with the conventional technology in various exploration objects. A high-precision search enables anyone to easily confirm the position of the buried object visually.
[0112]
Furthermore, according to the embedded object exploration processing method or apparatus according to the present invention, the embedded object continuously extending in the predetermined length direction in the investigation object, the transmission wave of the electromagnetic wave or the ultrasonic wave from the surface of the investigation object. Irradiate, receive the reflected wave of the transmitted wave, in the buried object search processing method or apparatus to search based on the data of the reflected wave, based on the data of the received reflected wave, orthogonal to the surface of the object to be searched Generating image data having the density data of at least one cross-section, and removing the image data having no buried object from the generated image data using the knowledge that the buried object extends continuously. Generating candidate point image data of at least one cross section indicating a candidate point of an embedded object, and virtually arranging the generated candidate point image data of at least one cross section in a three-dimensional space. Based on the candidate point image data stored in the image memory, the position estimation processing parameters are adapted to increase the degree of conformity of the position estimation processing parameters using a genetic algorithm based on the candidate point image data stored in the image memory. While controlling, the position of the buried object is estimated by connecting the candidate points using the knowledge that the buried object continuously extends, and based on the estimated position of the buried object, Is generated and output in the form of a three-dimensional image. Therefore, the embedded object exploration process can be executed while adaptively controlling the measurement parameters used when executing the embedded object exploration process to the optimum value, and the embedded object can be easily compared with the conventional technology in various exploration objects. A high-precision search enables anyone to easily confirm the position of the buried object visually.
[Brief description of the drawings]
FIG. 1 is a flowchart illustrating a measurement process including a measurement parameter adaptive control process according to a first embodiment of the present invention.
FIG. 2 is a block diagram showing a configuration of a buried object search system according to a second embodiment of the present invention.
FIG. 3 is a flowchart showing an embedded object exploration image processing (main routine) executed by a CPU 20 of the image processing apparatus 10 of FIG. 2;
FIG. 4 is a flowchart showing an embedding position candidate estimating process (step S2) based on reflected wave signal data at the time of scanning in the X direction, which is a subroutine of FIG.
FIG. 5 is a flowchart showing an embedding position candidate estimating process (step S3) based on reflected wave signal data at the time of scanning in the Y direction, which is a subroutine of FIG.
FIG. 6 is a flowchart showing a process (step S15) of estimating an embedding position candidate by searching for a minimum density point based on image data of an XZ slice of the embedding position candidate, which is a subroutine of FIG.
FIG. 7 is a flowchart showing a process (step S71) of estimating a burying position candidate by searching for a minimum point of light and shade, which is a subroutine of FIG.
8 is a flowchart showing a process (step S72) of estimating an embedding position candidate by looking at a hyperbolic pattern which is a subroutine of FIG.
FIG. 9 is a flowchart showing a process (step S73) of integrating the processes of estimating the two embedding position candidates by weighting, which are subroutines of FIG.
FIG. 10 is a flowchart showing a first part of a connection search process (step S5) which is a subroutine of FIG. 3;
FIG. 11 is a flowchart showing a second part of the connection search process (step S5), which is a subroutine of FIG.
FIG. 12 is a flowchart showing a buried object extraction process (step S6) which is a subroutine of FIG. 3;
FIG. 13 is a flowchart showing a process (step S8) of adaptively setting parameters for estimation processing using a genetic algorithm, which is a subroutine of FIG.
FIG. 14 is a spectrum diagram showing a frequency spectrum of a transmission wave signal used in the embedded object exploration system of FIG. 2;
15 is a signal waveform diagram showing a waveform of a transmission pulse of the transmission wave signal of FIG.
FIG. 16 is a plan view showing a scanning method of search used in the embedded object search system of FIG. 2;
FIG. 17 is a front view showing a measurement principle of a search device provided with a buried object search transmitting / receiving device used in the buried object search system of FIG. 2;
FIG. 18 is a diagram showing a hyperbolic pattern of a reflected wave group measured by the embedded object exploration system of FIG. 2 and a photograph of an image.
19 (a) is a waveform diagram of a received waveform measured by the buried object detection system of FIG. 2, (b) is a photograph of an X-direction search result image, and (c) is a Y-direction thereof. 5 is a photograph of a search result image of FIG.
20 (a) is a photograph of a search result image measured by the embedded object search system of FIG. 2, and FIG. 20 (b) is a waveform diagram showing a waveform on the La line in FIG. 20 (a).
21A is a photograph of a search result image measured by the buried object search system of FIG. 2, FIG. 21B is a waveform diagram of a waveform on the Lb line of FIG. 21A, and FIG. It is an enlarged view of a mutual interference part.
FIG. 22 is a graph showing a concentration distribution measured by the embedded object exploration system of FIG. 2;
23A and 23B are diagrams showing extraction results of candidate regions measured by the embedded object exploration system of FIG. 2, wherein FIG. 23A is a photograph of the original image, and FIG. 23B is an image of the processing result; FIG.
24 (a) and (b) are waveform diagrams each showing a signal waveform measured by the embedded object exploration system of FIG. 1, (c) is a diagram showing an image before processing, and (d) FIG. 7 is a diagram showing an image after processing.
25 (a) is a diagram showing an image of a labeling result measured by the embedded object exploration system of FIG. 2, and FIG. 25 (b) is a diagram showing an image of the buried position estimation result.
26 is a three-dimensional perspective view schematically showing a region N4 of FIG. 19 measured by the embedded object exploration system of FIG. 2;
27A and 27B are an image and a three-dimensional perspective view illustrating an example in which the embedded position cannot be estimated by the minimum point search, which is measured by the embedded object search system of FIG. 2;
FIG. 28 is a perspective view showing a connection process of estimated positions executed by the embedded object exploration system of FIG. 2;
29A and 29B are membership functions for extracting a buried pipe used in the embedded object exploration system of FIG. 2, wherein FIG. 29A is a graph of a membership function relating to a distance from an outer periphery, and FIG. It is a graph of a ship function.
30A and 30B are graphs showing characteristics of concentration used in the buried object detection system of FIG. 2, wherein FIG. 30A shows a propagation result with respect to a survey distance in a case of a buried pipe, and a result of a search relating to a reflected wave group of electromagnetic waves; FIG. 7B is a diagram showing a grayscale image of FIG. 5 and a graph showing the density with respect to the investigation distance in the grayscale image. FIG. FIG. 3 is a diagram showing a gray-scale image and a graph showing the density with respect to a survey distance in the gray-scale image.
31A and 31B are membership functions of concentration knowledge used in the buried object exploration system of FIG. 2, wherein FIG. 31A is a graph of an input membership function, and FIG. 31B is a graph of an output membership function. is there.
32 is a diagram showing characteristics of a hyperbolic shape used in the embedded object exploration system of FIG. 2, wherein FIG. 32 (a) shows a grayscale image of an exploration result related to a reflected wave group of electromagnetic waves, showing a propagation time with respect to an investigation distance. (B) is a diagram showing a hyperbolic shape in a grayscale image of the search result, and (c) is a diagram showing a theoretical hyperbola thereof.
FIG. 33 is a membership function of hyperbolic shape knowledge used in the embedded object exploration system of FIG. 2, (a) is a graph of an input membership function, and (b) is a graph of an output membership function. It is.
34 is a diagram illustrating a weighting coefficient w used in the processing in step S85 in FIG. 9 with respect to the depth used in the embedded object exploration system in FIG. 2;_i, W_h6 is a graph showing a function of.
FIG. 35 is a diagram showing an evaluation method using the fitness in the evaluation process (step S91) of FIG. 13, wherein (a) is a diagram showing a search result image of a buried pipe when the fitness is low; (B) is a diagram showing a search result image of a buried pipe when the degree of matching is high.
36 is a diagram illustrating a selection process using a roulette wheel selection method in the selection process (step S92) of FIG. 13; FIG. 36 (a) is a diagram illustrating an example of data of each individual in the selection process; ) Is a diagram showing the fitness g for each individual, (c) is a diagram showing the selection probability P for each individual, and (d) is a diagram showing a pie chart of the roulette selection probability.
FIG. 37 is a diagram showing an example of a one-point crossover process in the crossover process (step S93) of FIG. 13; (a) is a diagram showing data of two selected solids before the crossover process; ) Is a diagram showing data of two selected solids after the crossover processing.
FIG. 38 is a diagram illustrating an example of a process in the mutation process (step S93) in FIG. 13;
FIG. 39 is a graph showing the transition of the degree of conformity with the number of generations T in the embodiment of the embedded object exploration system of FIG. 2;
40 is a diagram showing search results in the first embodiment of the embedded object exploration system of FIG. 2, wherein FIG. 40 (a) is a diagram showing measurement results of an X-ray image according to a conventional technique, and FIG. It is a figure which shows the search result image before applying the method which concerns on embodiment, (c) is a figure which shows the search result image after applying the method which concerns on this embodiment.
41A and 41B are diagrams showing search results in the second embodiment of the buried object exploration system of FIG. 2, wherein FIG. 41A is a diagram showing measurement results of an X-ray image according to a conventional technique, and FIG. It is a figure which shows the search result image before applying the method which concerns on embodiment, (c) is a figure which shows the search result image after applying the method which concerns on this embodiment.
42 (a) and 42 (b) are diagrams showing search results in the third embodiment of the embedded object exploration system of FIG. 2; FIG. 42 (a) is a diagram showing measurement results of an X-ray image according to the prior art; It is a figure which shows the search result image before applying the method which concerns on embodiment, (c) is a figure which shows the search result image after applying the method which concerns on this embodiment.
[Explanation of symbols]
1. Electromagnetic wave transmitter / receiver for buried object exploration,
1R ... wheels,
1RR ... body,
2 ... Transmission antenna,
3 ... receiving antenna,
4 ... Transmitter,
5 ... Receiver,
6 ... Controller,
7 ... communication interface,
10. Image processing device,
20 ... CPU,
21 ... ROM,
22 ... RAM,
23 ... Image memory,
24 ... program memory,
25 processing memory,
26 ... Reception memory,
30 ... bus,
31 ... keyboard interface,
32 ... Mouse interface,
33 ... Display interface,
34 ... Printer interface,
35 ... Drive device interface,
41 ... keyboard,
42 ... mouse,
43 ... CRT display,
44 ... Printer,
45 ... CD-ROM drive device,
45a ... CD-ROM,
51 ... communication cable,
61 Communication interface.
90 ... buried object,
91 ... Exploration object,
92 ... Exploration surface.

Claims (18)

A first step of receiving the measured input data;
A second step of setting initial values of measurement parameters used at the time of measurement;
A third step of performing a predetermined measurement process using a measurement parameter set based on the received input data to obtain a measurement process result;
A fourth step of adaptively controlling and setting the measurement parameters so that the degree of conformity of the measurement parameters is increased using a genetic algorithm based on the obtained measurement processing results;
After the fourth step, it is determined whether or not a predetermined end condition is satisfied. When the end condition is not satisfied, the third and fourth steps are repeated, while when the end condition is satisfied, And a fifth step of outputting the result of the measurement process in the last third step as output data. A buried object extending continuously in a predetermined length direction in the exploration object emits a transmission wave of an electromagnetic wave or an ultrasonic wave from the surface of the exploration object, receives a reflection wave of the transmission wave, and receives data of the reflection wave. In the buried object exploration processing method for exploring based on
Based on the received reflected wave data, extracting predetermined characteristic parameter data for the search for the buried object,
Based on the extracted feature parameter data, using a genetic algorithm, the embedded object is continuously controlled while adaptively controlling the position estimation processing parameter so that the degree of conformity of the position estimation processing parameter is increased. Estimating the position of the buried object using the knowledge of extending;
Generating and outputting an image of the buried object in the form of a three-dimensional image based on the estimated position of the buried object. A buried object extending continuously in a predetermined length direction in the exploration object emits a transmission wave of an electromagnetic wave or an ultrasonic wave from the surface of the exploration object, receives a reflection wave of the transmission wave, and receives data of the reflection wave. In the buried object exploration processing method for exploring based on
Based on the received reflected wave data, generate image data having density data of at least one cross section orthogonal to the surface of the exploration object, and use the knowledge that the buried object extends continuously. Generating candidate point image data of at least one cross section indicating candidate points of the buried object by removing image data having no buried object from the generated image data;
Storing the generated candidate point image data of at least one cross section in an image memory so as to be virtually arranged in a three-dimensional space;
Based on the candidate point image data stored in the image memory, using a genetic algorithm, while adaptively controlling the position estimation processing parameters so as to increase the degree of conformity of the position estimation processing parameters, the embedded object is Estimating the position of the buried object by connecting the candidate points using the knowledge that it extends continuously;
Generating and outputting an image of the buried object in the form of a three-dimensional image based on the estimated position of the buried object. The method for exploring and processing a buried object according to claim 3,
After the step of estimating the position of the buried object,
(A) a first rule that the probability of being a buried object is high when both ends of the buried object are close to the end of the sought object and the depth of the buried object is not located between the layers of the sought object;
(B) a second rule that the probability that the object is a buried object is low when both ends of the buried object are far from the end of the object to be searched and the depth of the object is located between the cover and the thickness of the object to be searched. Using fuzzy rules,
Based on the estimated position of the buried object, it is determined whether the estimated position of the buried object is a buried object, and when it is determined that the buried object is not a buried object, the position of the estimated buried object is determined. A buried object exploration processing method, further comprising the step of removing from data. In the embedded object exploration processing method according to claim 3 or 4, the step of estimating the position of the embedded object includes:
The knowledge of the density in the candidate point image data stored in the image memory is expressed, and the degree of the embedding position candidate based on the average angle of the density change is determined using a predetermined first membership function indicating the degree of the embedding position candidate. Calculating a degree;
Represents the knowledge of the hyperbolic shape in the candidate point image data stored in the image memory, and indicates the degree of the embedding position candidate based on the degree of coincidence between the hyperbola and the theoretical hyperbola obtained by referring to the density in the candidate point image data. Calculating the degree of the second buried object using a predetermined second membership function as shown;
A degree of a third buried object is calculated by integrating the calculated degree of the first buried object and the calculated degree of the second buried object by a linear combination using a function of a predetermined weighting coefficient. Steps and
Estimating the position of the buried object by determining whether or not the image data is a buried object candidate based on the third buried object degree. 6. The embedded object exploration processing method according to claim 5, wherein the position estimation processing parameters include a parameter for defining the first membership function, a parameter for defining the second membership function, And a parameter for defining a function of the weighting coefficient. 7. The buried object exploration processing method according to claim 2, wherein the fitness is a length of each buried pipe estimated and extracted from candidate point image data stored in the image memory. A buried object exploration processing method characterized by the total sum. Receiving means for receiving the measured input data;
Setting means for setting initial values of measurement parameters used at the time of measurement;
Measuring means for obtaining a measurement processing result by executing a predetermined measurement processing using a measurement parameter set based on the received input data,
Using a genetic algorithm based on the obtained measurement processing results, adaptive control means for adaptively controlling and setting the measurement parameters so that the degree of conformity of the measurement parameters is high,
After the processing of the adaptive control means, it is determined whether a predetermined end condition is satisfied. When the end condition is not satisfied, the processing of the measuring means and the processing of the adaptive control means are repeated, And a result output means for outputting, as output data, a measurement processing result in the processing of the last measurement means when the condition (1) is satisfied. A buried object extending continuously in a predetermined length direction in the exploration object emits a transmission wave of an electromagnetic wave or an ultrasonic wave from the surface of the exploration object, receives a reflection wave of the transmission wave, and receives data of the reflection wave. In a buried object exploration processing device that performs exploration based on
Based on the received reflected wave data, extracting means for extracting data of predetermined characteristic parameters for exploration of the buried object,
Based on the extracted feature parameter data, using a genetic algorithm, the embedded object is continuously controlled while adaptively controlling the position estimation processing parameter so that the degree of conformity of the position estimation processing parameter is increased. Estimating means for estimating the position of the buried object using the knowledge of extending,
Output means for generating and outputting an image of the buried object in the form of a three-dimensional image based on the estimated position of the buried object. A buried object extending continuously in a predetermined length direction in the exploration object emits a transmission wave of an electromagnetic wave or an ultrasonic wave from the surface of the exploration object, receives a reflection wave of the transmission wave, and receives data of the reflection wave. In a buried object exploration processing device that performs exploration based on
Based on the received reflected wave data, generate image data having density data of at least one cross section orthogonal to the surface of the exploration object, and use the knowledge that the buried object extends continuously. Generating means for generating candidate point image data of at least one cross section indicating a candidate point of the buried object by removing image data having no buried object from the generated image data;
Storage means for storing the generated candidate point image data of at least one section in an image memory so as to be virtually arranged in a three-dimensional space;
Based on the candidate point image data stored in the image memory, using a genetic algorithm, while adaptively controlling the position estimation processing parameters so as to increase the degree of conformity of the position estimation processing parameters, the embedded object is Position estimating means for estimating the position of the buried object by connecting the candidate points using the knowledge of extending continuously,
Output means for generating and outputting an image of the buried object in the form of a three-dimensional image based on the estimated position of the buried object. The buried object detection processing device according to claim 10,
After the processing of the position estimating means,
(A) a first rule that the probability of being a buried object is high when both ends of the buried object are close to the end of the sought object and the depth of the buried object is not located between the layers of the sought object;
(B) a second rule that the probability that the object is a buried object is low when both ends of the buried object are far from the end of the object to be searched and the depth of the object is located between the cover and the thickness of the object to be searched. Using fuzzy rules,
Based on the estimated position of the buried object, it is determined whether or not the estimated position of the buried object is a buried object. A buried object exploration processing device, further comprising a removing means for removing from data. The buried object exploration processing device according to claim 10 or 11, wherein the position estimating means includes:
The knowledge of the density in the candidate point image data stored in the image memory is expressed, and the degree of the embedding position candidate based on the average angle of the density change is determined using a predetermined first membership function indicating the degree of the embedding position candidate. First calculating means for calculating the degree;
Represents the knowledge of the hyperbolic shape in the candidate point image data stored in the image memory, and indicates the degree of the embedding position candidate based on the degree of coincidence between the hyperbola and the theoretical hyperbola obtained by referring to the density in the candidate point image data. Second calculating means for calculating the degree of the second buried object using a predetermined second membership function shown;
A degree of a third buried object is calculated by integrating the calculated degree of the first buried object and the calculated degree of the second buried object by a linear combination using a function of a predetermined weighting coefficient. A third calculating means;
A buried object position estimating means for estimating the position of the buried object by judging whether or not the image data is a buried object candidate based on the degree of the third buried object. Object exploration processing equipment. 13. The embedded object exploration processing apparatus according to claim 12, wherein the position estimation processing parameters include a parameter for defining the first membership function, and a parameter for defining the second membership function. And a parameter for defining a function of the weighting coefficient. 14. The buried object exploration processing apparatus according to claim 9, wherein the fitness is a length of each buried pipe estimated and extracted from the candidate point image data stored in the image memory. A buried object exploration processing device characterized by the total sum. An adaptive control type measurement processing program, comprising the steps of the adaptive control type measurement processing method according to claim 1. A buried object search processing program comprising each step of the buried object search processing method according to any one of claims 2 to 7. A recording medium on which the adaptive control type measurement processing program according to claim 15 is recorded. A recording medium on which the program for processing a process for searching for a buried object according to claim 16 is recorded.

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