JPS6340843A - Surface flaw detection - Google Patents
Surface flaw detectionInfo
- Publication number
- JPS6340843A JPS6340843A JP18384886A JP18384886A JPS6340843A JP S6340843 A JPS6340843 A JP S6340843A JP 18384886 A JP18384886 A JP 18384886A JP 18384886 A JP18384886 A JP 18384886A JP S6340843 A JPS6340843 A JP S6340843A
- Authority
- JP
- Japan
- Prior art keywords
- flaw
- signal
- image
- flaw detection
- inspected
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000001514 detection method Methods 0.000 title claims abstract description 37
- 239000000463 material Substances 0.000 claims abstract description 26
- 238000012545 processing Methods 0.000 claims abstract description 26
- 238000000034 method Methods 0.000 claims description 18
- 238000005259 measurement Methods 0.000 claims description 10
- 238000000605 extraction Methods 0.000 abstract description 3
- 238000013139 quantization Methods 0.000 abstract description 2
- 230000007547 defect Effects 0.000 description 13
- 238000010586 diagram Methods 0.000 description 5
- 238000007689 inspection Methods 0.000 description 5
- 238000003909 pattern recognition Methods 0.000 description 4
- 229910000831 Steel Inorganic materials 0.000 description 3
- 230000006870 function Effects 0.000 description 3
- 239000010959 steel Substances 0.000 description 3
- 230000001988 toxicity Effects 0.000 description 3
- 231100000419 toxicity Toxicity 0.000 description 3
- 229910001220 stainless steel Inorganic materials 0.000 description 2
- 239000010935 stainless steel Substances 0.000 description 2
- 238000011179 visual inspection Methods 0.000 description 2
- 230000000694 effects Effects 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 239000000284 extract Substances 0.000 description 1
- 238000005286 illumination Methods 0.000 description 1
- 238000000275 quality assurance Methods 0.000 description 1
- 230000004044 response Effects 0.000 description 1
- 238000005070 sampling Methods 0.000 description 1
- 230000035945 sensitivity Effects 0.000 description 1
- 238000012360 testing method Methods 0.000 description 1
- 230000000007 visual effect Effects 0.000 description 1
Landscapes
- Investigating Materials By The Use Of Optical Means Adapted For Particular Applications (AREA)
Abstract
Description
【発明の詳細な説明】
〔産業上の利用分野〕
本発明は走行中の被検査材、例えば鋼片や鋼板等の表面
疵を検出する方法に関するものである。DETAILED DESCRIPTION OF THE INVENTION [Field of Industrial Application] The present invention relates to a method for detecting surface flaws on a moving inspected material, such as a steel piece or a steel plate.
走行する被検査材、例えば圧延鋼板の表面疵検査方法と
しては、第5図に示すように、レーザー発振器1からの
レーザー光IAを回転鏡2を用いて被検査材3の移動方
向と垂直な方向に走査させ、被検査材3の表面からの反
射光をレンズ4で集光して、光電変換器5で電気信号に
変換して検査する方法や、第6図に示すように被検査材
3の表面に一定強度の照明6を照射し、被検査材3の移
動方向と垂直な方向に視野を設定したリニアアレイセン
サー7で走査して検査する方法等、−次元スキャナーを
用いる方法が周知である。As shown in FIG. 5, a method for inspecting the surface of a moving inspected material, such as a rolled steel plate, involves directing the laser beam IA from a laser oscillator 1 in a direction perpendicular to the moving direction of the inspected material 3 using a rotating mirror 2. There is a method in which the reflected light from the surface of the material to be inspected 3 is focused by a lens 4 and converted into an electrical signal by a photoelectric converter 5 for inspection. A method using a -dimensional scanner is well known, such as a method in which the surface of the material 3 is irradiated with a constant intensity illumination 6 and inspected by scanning with a linear array sensor 7 whose field of view is set perpendicular to the direction of movement of the material 3 to be inspected. It is.
上記の検出方法で用いられてきた表面疵検出方法は、例
えば上記スキャナーの走査信号あるいは走査信号の微分
信号を所定の疵信号レベルと比較する事により疵信号レ
ベルを越えた場合に疵であると判定し、更に走査信号あ
るいはそ、の微分信号の大きさで疵の有害度を判定する
といったものである。製品の表面検査の主たる目的の1
つは需要家に対する品質保証であり、有害疵の見逃しく
未検出)は大きな問題となる。そこで検出感度を上げる
ため上記の疵検出レベルを低くすると有害度が充分低い
検出する必要のない疵を検出(過検出)したり、疵でな
いものを疵として検出(誤検出〉したりする事になり、
上記の検出方法では検出装置の過検出、誤検出を抑え検
出率(検出装置で検出した検出すべき疵の数/検出すべ
き疵の数)を上げる事が困難であった。これは疵のを害
度が単に走査信号あるいはその微分信号の大小のみでは
決められない事、更にその判定基準が疵の種類毎に異な
る事に因る。The surface flaw detection method used in the above detection method compares the scanning signal of the scanner or the differential signal of the scanning signal with a predetermined flaw signal level, and detects a flaw if it exceeds the flaw signal level. Then, the degree of harmfulness of the flaw is determined based on the magnitude of the scanning signal or its differential signal. One of the main purposes of product surface inspection
The first is quality assurance for consumers, and harmful defects (missed or undetected) are a major problem. Therefore, in order to increase the detection sensitivity, lowering the above flaw detection level may result in detecting flaws that are sufficiently low in harmfulness that do not need to be detected (overdetection), or detecting things that are not flaws as flaws (false detection). Become,
In the above detection method, it is difficult to suppress over-detection and erroneous detection by the detection device and increase the detection rate (number of flaws detected by the detection device/number of flaws to be detected). This is because the degree of damage to a flaw cannot be determined simply by the magnitude of the scanning signal or its differential signal, and furthermore, the criteria for determining this differ depending on the type of flaw.
従来、表面疵検査は検査員による目視検査が行なわれて
いた背景から自動表面疵検査の良否は目視検査結果との
対比で評価される場合が多い。然るに検定員は被検査材
表面に発生する疵を2次元の画像として捉え、直観的に
パターン認識を行なって疵の種類を判定し更に疵種毎の
有害度を判定している。これらの状況より表面疵検査の
自動化においては、疵を画像として捉え画像処理、パタ
ーン認識処理等の高度の信号処理を行ない、まず疵の種
類を判定して次にその有害度を判定する方法が考えられ
る。Conventionally, surface flaw inspections have been performed visually by inspectors, so the quality of automatic surface flaw inspections is often evaluated by comparing the results with visual inspections. However, the examiner captures the flaws occurring on the surface of the inspected material as a two-dimensional image, intuitively performs pattern recognition to determine the type of flaw, and further determines the degree of harmfulness of each type of flaw. Under these circumstances, in automating surface flaw inspection, it is necessary to capture flaws as images and perform advanced signal processing such as image processing and pattern recognition processing to first determine the type of flaw and then determine its degree of harmfulness. Conceivable.
この際の問題として、被検査材表面全体の画像を処理し
ようとすると処理すべきデータ量は膨大となり、いかに
高性能の信号処理装置を用いたとしても実用に供する時
間内では処理しきれない。The problem with this case is that when attempting to process an image of the entire surface of the material to be inspected, the amount of data to be processed becomes enormous, and no matter how high-performance a signal processing device is used, it cannot be processed within a practical time.
従って処理すべきデータ量を必要最低限に抑える必要が
ある。その方法として、スキャナーの走査信号を数レベ
ルに量子化して、被検査材表面の疵分布パターンを作り
、被検査材の移動方向に一定区間長の範囲で長さ方向と
幅方向それぞれのヒストグラムを作成して、そのヒスト
グラムより抽出した特@量を用いて疵の種類および有害
度を判定する方法(特開昭59−99238 >や、被
検査材表面をTVカメラで走査し走査信号の微分値が所
定の疵信号レベルを越えた画面上の位置を指定点とした
限定領域の画像を記憶装置に記憶し、更に画像処理、パ
ターン認識処理を行なって疵の真偽および疵の種類と大
小を検出する方法(特開昭59−138904)等が提
案されている。Therefore, it is necessary to suppress the amount of data to be processed to the minimum necessary. As a method, the scanning signal of the scanner is quantized into several levels to create a flaw distribution pattern on the surface of the material to be inspected, and histograms are created in the length and width directions within a certain section length in the direction of movement of the material to be inspected. A method of determining the type and harmfulness of defects using the characteristic quantities extracted from the histogram (Japanese Unexamined Patent Publication No. 59-99238), or scanning the surface of the material to be inspected with a TV camera and determining the differential value of the scanning signal. An image of a limited area with the specified point on the screen where the signal exceeds a predetermined flaw signal level is stored in a storage device, and further image processing and pattern recognition processing are performed to determine the authenticity of the flaw and the type and size of the flaw. A detection method (Japanese Unexamined Patent Publication No. 59-138904) and the like have been proposed.
被検査材が例えばステンレス鋼板の如く美麗な表面性状
を要求される場合、検出すべき表面疵の個々の大きさは
非常に小さいものもあり、また判定すべき疵の種類は非
常に多く、疵の有害度も細分化されている。一方、疵の
発生状況は単独で発生する場合、複数の疵が局部的に集
中して発生する場合、あるいは広範囲にわたって発生す
る場合等、様々であり疵の種類によってはその分布状態
から有害度が決まる場合がある。然るに上記従来技術の
前者の方法ではスキャナーの走査幅と所定の区間長で決
まる比較的広い範囲全面の画像を実時間で処理するため
画像の分解能(画素数)が低く、画像を処理して得られ
る特徴量が限られ、故に大まかな疵の種類判定しか行な
えないという問題がある。また後者の方法では限定領域
の画像を処理対象とするため実時間で高分解能の画像の
処理が可能となるも、画像の優先切り出し機能を有さな
いため、疵の発生頻度に応じて記憶装置の容量を増やす
事によりある程度の見逃しを防止出来たとしても実時間
処理性の低下は免れない。更に比較的狭い限定領域の画
像だけでは疵の有害度の正確な判定が行なえない等の問
題がある。When the inspected material is required to have a beautiful surface quality, such as a stainless steel plate, the individual size of the surface flaws to be detected may be very small, and there are many types of flaws to be judged. The level of toxicity is also subdivided. On the other hand, there are various situations in which flaws occur, such as when they occur singly, when multiple flaws occur locally, or when they occur over a wide area. It may be decided. However, in the former method of the prior art described above, an image covering a relatively wide area determined by the scanning width of the scanner and a predetermined interval length is processed in real time, so the resolution (number of pixels) of the image is low, and the result obtained by processing the image is low. There is a problem in that the number of feature values that can be detected is limited, and therefore only a rough type of flaw can be determined. In addition, in the latter method, images in a limited area are processed, so it is possible to process high-resolution images in real time, but since it does not have a priority image cutting function, storage Even if it is possible to prevent some oversights by increasing the capacity of , real-time processing performance will inevitably deteriorate. Furthermore, there is a problem in that it is not possible to accurately determine the degree of harmfulness of a flaw using only an image of a relatively narrow limited area.
前記の如く、精度の良い表面疵検出を行なうためには高
度の信号処理を実時間で行なう必要があるが、処理すべ
きデータ量を少なくするために、全体的であるが大まか
な情報のみ、あるいは詳細であるがご(限られた範囲の
情報のみを用いたのでは疵の種類や有害度の判定に必要
な充分な情報が得られず、また時系列に発生する疵の画
像を順に処理すると重大欠陥の見逃しや実時間処理性の
低下を招き、実用的な表面疵検出方法を供し難い。As mentioned above, in order to detect surface flaws with high precision, it is necessary to perform sophisticated signal processing in real time, but in order to reduce the amount of data to be processed, only general but general information is processed. Or if it is detailed (using only a limited range of information does not provide enough information to judge the type of flaw or its degree of toxicity), images of flaws that occur in chronological order may not be processed. This results in serious defects being overlooked and real-time processing performance being reduced, making it difficult to provide a practical surface flaw detection method.
本発明は、これらの問題を解決すべ(、被検歪材全面を
走査し疵と疑わしきものを検知し、その1疵らしきもの
が任意の範囲内に複数検知された場合はそれらの中で最
も有害度が高いと思われるものを中心とした一定の広さ
領域の詳細な疵部画像を優先的に抽出する事により、重
大欠陥を見逃す事なく処理すべきデータ量を必要最低限
に抑え、高度な信号処理を行なって疵の真偽や疵の種類
および有害度の判定を実時間で実行することを可能とす
る表面疵検出方法であり、また上記の詳細な疵部画像の
処理とあわせてその背景となる大まかな疵分布画像を構
成、処理する事により、疵の種類および有害度の判定の
精度を更に高める事を可能とする表面疵検出方法である
。The present invention solves these problems by scanning the entire surface of the strained material to be tested and detecting defects that are suspected to be defects. By preferentially extracting detailed images of defects in a certain area, mainly those that are considered to be highly harmful, the amount of data to be processed can be kept to the minimum necessary without overlooking serious defects. This is a surface flaw detection method that performs advanced signal processing to make it possible to determine the authenticity of flaws, the type of flaw, and the degree of harmfulness in real time. This is a surface flaw detection method that makes it possible to further improve the accuracy of determining the type and degree of harmfulness of flaws by constructing and processing a rough flaw distribution image that serves as the background.
C問題点を解決するだめの手段1作用〕第1図に本発明
を実施するための装置構成例を示す。−次元スキャナー
7を用いて被検査材3の表面を走査する。他方、タッチ
ロール8およびパルスジェネレータ9を介して得られる
被検査材の移動量に応じた測長パルス12とスキャナー
7の走査開始信号11に同期して、書き込みコントロー
ラ装置15が量子化装置13に量子化信号を出力する事
により走査信号10が量子化され、書き込ミコントロー
ラ装置15は測長パルス12に同期して1ライン毎にシ
フトしながらフレームメモリ14に走査信号10の量子
化データを記憶する。Means 1 for Solving Problem C] FIG. 1 shows an example of the configuration of an apparatus for carrying out the present invention. - Scanning the surface of the inspected material 3 using the dimensional scanner 7; On the other hand, in synchronization with the length measurement pulse 12 corresponding to the amount of movement of the inspected material obtained via the touch roll 8 and pulse generator 9 and the scan start signal 11 of the scanner 7, the writing controller device 15 sends a signal to the quantization device 13. By outputting a quantized signal, the scanning signal 10 is quantized, and the write microcontroller 15 transfers the quantized data of the scanning signal 10 to the frame memory 14 while shifting line by line in synchronization with the length measurement pulse 12. Remember.
一方、走査信号lOは微分装置16にて微分され、比較
装置17で所定の多段階の疵信号レベルと比較され、走
査信号10の微分信号が越えた該疵信号レベルに対応す
るレベルの疵検知信号18を画像切り出しコントローラ
装置19に出力する事により、疵と疑しいものの発生を
知らせる。On the other hand, the scanning signal lO is differentiated by a differentiator 16 and compared with a predetermined multi-stage flaw signal level by a comparator 17, and a flaw is detected at a level corresponding to the flaw signal level exceeded by the differential signal of the scanning signal 10. By outputting the signal 18 to the image cutout controller device 19, the occurrence of something suspected to be a flaw is notified.
画像切り出しコントローラ19は疵検知信号18を受け
て、疵と疑しいものが発生した被検査材表面の位置に相
当するフレームメモリ14上の位置を中心としたn×m
画素の疵部画像を切り出すべく、所定の測定パルス数の
間、待機する。疵部画像の切り出し待機中に、よりレベ
ルの高い疵検知信号が画像切り出しコントローラ装置1
9に入力された場合は、前者の疵部画像の切り出しを中
止し、後者の疵検知信号に対応する疵部画像の切り出し
を行なうべく再び所定の測長パルス数の間待機し、その
間に更にレベルの高い疵検知信号が入力されなければ後
者の疵部画像を切り出して信号処理装置20に伝送する
。信号処理装置20では画像処理、パターン認識処理を
行なって疵の真偽および疵の種類と有害度を判定する。In response to the flaw detection signal 18, the image cutout controller 19 extracts n×m images centered on the position on the frame memory 14 corresponding to the position on the surface of the inspected material where a suspected flaw has occurred.
In order to cut out the pixel defect image, the system waits for a predetermined number of measurement pulses. While waiting to cut out the flaw image, a higher level flaw detection signal is sent to the image cutout controller device 1.
9, the cutout of the former flaw image is stopped, and in order to cut out the flaw image corresponding to the latter flaw detection signal, the system waits again for a predetermined number of length measurement pulses, and during that time, further processing is performed. If a high-level flaw detection signal is not input, the latter flaw image is cut out and transmitted to the signal processing device 20. The signal processing device 20 performs image processing and pattern recognition processing to determine the authenticity of the flaw, as well as the type and degree of harmfulness of the flaw.
疵部画像の切り出しにつき第2図により更に説明する。Cutting out the flaw image will be further explained with reference to FIG.
被検査表面のスキャナーの走査幅Wと任意の長さで囲ま
れる範囲21において、いま地点24に疵検知信号が発
生すると画像切り出しコントローラ19は地点24を中
心とする尿定領域25に相当するnXm画素の疵部画像
をフレームメモリ14上から切り出すべく地点24を含
むライン以降m′ ラインの間待機する。その間により
高いレベルの疵検知信号が発生しなければ限定領域25
に相当する疵部画像を切り出すが、例えば地点26に地
点24よりも高いレベルの疵検知信号が発生した場合、
前者の疵部画像の切り出しを中止し、地点26を中心と
する限定領域27に相当する疵部画像を切り出すべく再
度待機する。これらの処理を繰り返す事によりある走査
区間長の中で最もレベルの高い疵と疑しきものの画像の
切り出しが優先的に行なえる。When a flaw detection signal is generated at a point 24 in a range 21 surrounded by the scanning width W of the scanner and an arbitrary length of the surface to be inspected, the image cutting controller 19 detects n In order to extract a pixel defect image from the frame memory 14, the process waits for m' lines after the line including point 24. If a higher level flaw detection signal does not occur during that time, the limited area 25
For example, if a flaw detection signal of a higher level is generated at point 26 than at point 24,
The former cutting out of the flaw image is stopped, and the process waits again to cut out the flaw image corresponding to the limited area 27 centered on the point 26. By repeating these processes, it is possible to preferentially cut out an image of a suspected flaw with the highest level within a certain scanning section length.
第3図は本発明を実施するための他の装置構成例である
。第1図との相違点は、第1図における機能に加えて大
まかな疵分布情報を抽出、処理する機能を有する事であ
り、第1図における疵部画像の切り出しと並行してレベ
ル付けされた疵検知信号18を画素編集コントローラ装
置29に入力する。画素編集コントローラ装置29では
、画素カウント装置28にて走査開始信号11と測長パ
ルス12を基に作られた疵分布画像の画素サンプリング
信号に同期し、その画素に対応する範囲内にある紙検知
信号の中で最も高いレベルをその画素の値として画素メ
モリ31にその位置情報とともに記憶する。画素読み出
しコントローラ装置30は、画像切り出しコントローラ
装置19から入力される画像切り出し信号を受けて、切
り出された画像が含まれる一定区間長しく第4図)の範
囲内の疵分布データを画素メモリ31より読み出して信
号処理装置20に伝送する。信号処理装置20では、疵
分布データを用いて一定区間長のN×M画素の疵分布画
像を構成し、画像処理を行なって疵の背景情報を得、第
1図における庇部画像の処理結果とあわせて疵の種類お
よび有害度の判定に使用する。第4図に圧部・画像と疵
分布画像の関係を示す。ここで限定領域32の大きさく
幅W、長さ/)と庇部画像の画素数(n×m)および一
定の区間長の範囲33の長さしと疵分布画像33の画素
数(NxM)等のパラメータは、発生する疵の統計的性
質に基づき決定する。FIG. 3 shows another example of the configuration of an apparatus for carrying out the present invention. The difference from Fig. 1 is that in addition to the functions in Fig. 1, it has a function to extract and process rough flaw distribution information, and in parallel with cutting out the flaw image in Fig. 1, it is leveled. The flaw detection signal 18 is input to the pixel editing controller device 29. The pixel editing controller device 29 synchronizes with the pixel sampling signal of the flaw distribution image created by the pixel counting device 28 based on the scanning start signal 11 and length measurement pulse 12, and detects paper within the range corresponding to the pixel. The highest level among the signals is stored as the value of that pixel in the pixel memory 31 together with its position information. The pixel readout controller device 30 receives an image cutout signal input from the image cutout controller device 19, and reads flaw distribution data within a certain length of a certain section (FIG. 4) including the cutout image from the pixel memory 31. It is read out and transmitted to the signal processing device 20. The signal processing device 20 uses the flaw distribution data to construct a flaw distribution image of N×M pixels of a certain section length, performs image processing to obtain background information of flaws, and obtains the processing result of the eaves image in FIG. It is also used to determine the type of flaw and its degree of toxicity. FIG. 4 shows the relationship between the pressure area image and the flaw distribution image. Here, the size of the limited area 32 (width W, length /), the number of pixels of the eaves image (n x m), the length of the range 33 of a certain section length and the number of pixels of the flaw distribution image 33 (N x M) These parameters are determined based on the statistical properties of the defects that occur.
〔実施例〕
以下に本発明をステンレス鋼板製造における最終工程で
の表面疵検出に適用した例を示す。[Example] An example in which the present invention is applied to detecting surface flaws in the final process of producing stainless steel sheets will be shown below.
比較装置における疵信号レベルは±3レベル、限定領域
の被検査材表面上の面積は32鶴”×64鰭′、対応す
る庇部画像の画素数は64X64、濃淡レベルは256
レベル、画像切り出し待機ライン数は1ライン、フレー
ムメモリの容量は512画素×32ライン分、疵分布画
像が表わす被検査材表面上の区間長は1m、同画像の画
素数は64X200、濃淡レベルは3レベル等、諸量を
設定し、SUS 304.2B材、A feet幅×1
00m長とSUS 430、BA材、1m幅X100m
長の疵検出を実施した結果、前者の場合、疵検知数47
、信号処理により疵であると判定した数36、底部合計
長さ22mに対し目視検査による底部合計長さ22m、
一方、後者の場合、疵検知数193、信号処理により疵
であると判定した数63、底部合計長さ28mに対し目
視検査による底部合計長さ29mで、目視検査と良好な
一致性を有するものであった。The flaw signal level in the comparison device is ±3 levels, the area of the limited area on the surface of the inspected material is 32" x 64 fins, the number of pixels of the corresponding eaves image is 64 x 64, and the density level is 256.
level, the number of waiting lines for image extraction is 1 line, the capacity of the frame memory is 512 pixels x 32 lines, the section length on the surface of the inspected material represented by the flaw distribution image is 1 m, the number of pixels in the image is 64 x 200, and the gray level is Set various quantities such as 3 levels, SUS 304.2B material, A feet width x 1
00m length and SUS 430, BA material, 1m width x 100m
As a result of long flaw detection, in the former case, the number of flaws detected was 47.
, number 36 determined to be flaws by signal processing, total bottom length 22m by visual inspection,
On the other hand, in the latter case, the number of defects detected was 193, the number of defects determined to be defects by signal processing was 63, and the total length of the bottom part was 29 m compared to the total length of the bottom part of 28 m, which showed good agreement with the visual test. Met.
本発明を表面疵検出に用いる事により、重大疵を見逃す
事なく処理すべきデータ量を必要最低限に抑える事で高
度の信号処理が実時間で実行可能となるため、自動検出
としての実用性を保ちつつ大幅の精度向上が期待出来る
。By using the present invention for surface flaw detection, advanced signal processing can be executed in real time by minimizing the amount of data to be processed without overlooking serious flaws, making it practical for automatic detection. A significant improvement in accuracy can be expected while maintaining the same.
第1図、第3図は本発明を実施するための装置構成の一
例を示す図、第2図は本発明における庇部画像の優先切
り出しを説明する図、第4図は本発明における庇部画像
と疵分布画像の関係を説明する図、第5図、第6図は一
次元スキャナーを用いた表面疵検出方法例を示す図であ
る。1 and 3 are diagrams showing an example of the configuration of an apparatus for carrying out the present invention, FIG. 2 is a diagram illustrating preferential extraction of eaves images in the present invention, and FIG. 4 is a diagram showing an example of the eaves part image in the present invention. FIGS. 5 and 6, which are diagrams explaining the relationship between images and flaw distribution images, are diagrams showing an example of a surface flaw detection method using a one-dimensional scanner.
Claims (2)
面疵を検出する方法において、該スキャナーの走査信号
あるいは走査信号の微分信号を多段階の疵信号レベルと
比較する事により疵検知信号を得、一方該走査信号を被
検査材の移動量に応じた測長パルスと同期して量子化し
、1ラインずつシフトしながらフレームメモリに一定区
間長の被検査材表面の画像として記憶し、該疵検知信号
が発生した位置に相当するフレームメモリ上の位置を中
心にn×m画素の疵部画像を切り出すべく所定の該測長
パルス数の間待機し、該疵部画像の切り出し待機中に、
よりレベルの高い疵検知信号が発生したら該疵部画像の
切り出し待機を中止し、後者の疵検知信号に対応する疵
部画像を切り出すべく再び所定の該測長パルス数の間待
機し、所定の待機時間中に更にレベルの高い疵検知信号
が発生しなければ後者の疵部画像を切り出し、切り出さ
れた疵部画像を信号処理して疵の真偽および疵の種類お
よび有害度を判定する事を特徴とする表面疵検出方法。(1) In a method of detecting surface flaws on a moving inspected material using a one-dimensional scanner, a flaw detection signal is generated by comparing the scanning signal of the scanner or the differential signal of the scanning signal with multilevel flaw signal levels. On the other hand, the scanning signal is quantized in synchronization with a length measurement pulse corresponding to the amount of movement of the material to be inspected, and is stored in a frame memory as an image of the surface of the material to be inspected with a certain section length while shifting one line at a time. Waiting for a predetermined number of length measurement pulses to cut out a flaw image of n×m pixels centered on the position on the frame memory corresponding to the position where the flaw detection signal is generated, and waiting to cut out the flaw image. To,
When a higher level flaw detection signal is generated, the process stops waiting to cut out the flaw image, waits again for the predetermined number of length measurement pulses to cut out the flaw image corresponding to the latter flaw detection signal, and then waits for the predetermined number of length measurement pulses. If a higher-level flaw detection signal is not generated during the waiting time, the latter flaw image is cut out, and the cut out flaw image is signal-processed to determine the authenticity of the flaw, the type of flaw, and the degree of harmfulness. A surface flaw detection method characterized by:
ャナーの走査開始信号を基にして作られる疵部画像の画
素よりも大きな画素に対応する範囲内にある、疵検知信
号の中で最も高いレベルをその画素の値として画素メモ
リにその位置情報とともに記憶し、該疵部画像を含む被
検査材表面の一定区間長の疵分布画像を該画素メモリの
値より構成し、該疵分布画像を信号処理して得られる情
報をあわせて疵の種類および有害度の判定に使用する事
を特徴とする特許請求の範囲第1項記載の表面疵検出方
法。(2) Among the flaw detection signals that are within the range corresponding to pixels larger than the pixels of the flaw image created based on the length measurement pulse corresponding to the amount of movement of the inspected material and the scanning start signal of the scanner. The highest level is stored as the value of that pixel in a pixel memory along with its position information, and a flaw distribution image of a certain section length on the surface of the inspected material including the flaw image is constructed from the values of the pixel memory, and the flaw distribution is 2. The surface flaw detection method according to claim 1, wherein information obtained by signal processing the image is used to determine the type and degree of harmfulness of the flaw.
Priority Applications (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
JP18384886A JPS6340843A (en) | 1986-08-05 | 1986-08-05 | Surface flaw detection |
Applications Claiming Priority (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
JP18384886A JPS6340843A (en) | 1986-08-05 | 1986-08-05 | Surface flaw detection |
Publications (1)
Publication Number | Publication Date |
---|---|
JPS6340843A true JPS6340843A (en) | 1988-02-22 |
Family
ID=16142891
Family Applications (1)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
JP18384886A Pending JPS6340843A (en) | 1986-08-05 | 1986-08-05 | Surface flaw detection |
Country Status (1)
Country | Link |
---|---|
JP (1) | JPS6340843A (en) |
-
1986
- 1986-08-05 JP JP18384886A patent/JPS6340843A/en active Pending
Similar Documents
Publication | Publication Date | Title |
---|---|---|
JP4351522B2 (en) | Pattern defect inspection apparatus and pattern defect inspection method | |
US4378495A (en) | Method and apparatus for setup of inspection devices for glass bottles | |
JPH0328749A (en) | Online type structure inspection system | |
JPH04240510A (en) | Discriminating method of defect | |
JP2008139285A (en) | Construct using picture processing technique, and crack width measuring method of product | |
JP2000036044A (en) | Defect integrating processor and defect integrating processing method | |
US6456318B1 (en) | Defect inspection apparatus and method by comparing two pairs of areas adjacent to one another | |
JP2007003459A (en) | Flaw inspection device of image, visual examination device and flaw inspection method of image | |
JP2006113073A (en) | System and method for pattern defect inspection | |
CN113177925A (en) | Method for nondestructive detection of fruit surface defects | |
JPH07333197A (en) | Automatic surface flaw detector | |
JP4956077B2 (en) | Defect inspection apparatus and defect inspection method | |
JPS6340843A (en) | Surface flaw detection | |
JPH06103276B2 (en) | Surface inspection device | |
JP2007081513A (en) | Blot defect detecting method for solid-state imaging element | |
JPS626179B2 (en) | ||
Pearson et al. | Automatic inspection of artillery shell radiographs | |
JP2004125629A (en) | Defect detection apparatus | |
JPH04115147A (en) | Optical inspection apparatus | |
JPS6349180B2 (en) | ||
JPH0412257A (en) | Method and device for detecting linear flaw on steel plate | |
JPH04238207A (en) | Defect inspecting device | |
JP2633673B2 (en) | Surface inspection equipment | |
JPH01214743A (en) | Optical apparatus for checking | |
JP3022627B2 (en) | Defect inspection equipment |