JPH11172570A - Fabric inspection system - Google Patents

Fabric inspection system

Info

Publication number
JPH11172570A
JPH11172570A JP33195497A JP33195497A JPH11172570A JP H11172570 A JPH11172570 A JP H11172570A JP 33195497 A JP33195497 A JP 33195497A JP 33195497 A JP33195497 A JP 33195497A JP H11172570 A JPH11172570 A JP H11172570A
Authority
JP
Japan
Prior art keywords
yarn
image
woven fabric
light
woven
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.)
Granted
Application number
JP33195497A
Other languages
Japanese (ja)
Other versions
JP3013927B2 (en
Inventor
Takahiro Kubota
隆弘 窪田
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Toyobo Co Ltd
Original Assignee
Toyobo Co Ltd
Priority date (The priority date 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 date listed.)
Filing date
Publication date
Application filed by Toyobo Co Ltd filed Critical Toyobo Co Ltd
Priority to JP9331954A priority Critical patent/JP3013927B2/en
Publication of JPH11172570A publication Critical patent/JPH11172570A/en
Application granted granted Critical
Publication of JP3013927B2 publication Critical patent/JP3013927B2/en
Anticipated expiration legal-status Critical
Expired - Fee Related legal-status Critical Current

Links

Abstract

PROBLEM TO BE SOLVED: To provide the subject system enabling both warp and weft abnormalities to be detected simultaneously in high accuracy under the identical optical conditions independently of the textile weave and density, and also enabling such abnormalities to the manifested automatically at low cost. SOLUTION: This woven fabric inspection system comprises a light projection means for irradiating the surface of a fabric with a light of high directivity at an acute angle from the direction rectangular to the inspection direction for the fabric, a photographing means for taking picture through focusing the reflected light from the fabric, a means for automatically calculating the textile weave cycle and yarn inclination of a yarn in the inspection direction based on the image data afforded by the photographing means, a means for binary setting through division according to the magnitude correlation compared to a threshold value automatically set for the overall picture element data correspondingly to the denseness levels of the image data, and an abnormality judgment means which sets a pair of rectangular areas within a binary image area produced by the preceding means based on the information afforded by the automatically calculating means, mutually compares statistics extracted from the image data within the above area, and, as a result, judges as 'abnormal' when a threshold value indicating a limit at which a value is considered as 'normal' is exceeded by the comparison result.

Description

【発明の詳細な説明】DETAILED DESCRIPTION OF THE INVENTION

【0001】[0001]

【発明の属する技術分野】本発明は製織中の織布又は織
り上がりの織布の欠陥の有無を自動検査する織布の検反
装置に関する。
BACKGROUND OF THE INVENTION 1. Field of the Invention The present invention relates to a woven cloth inspection apparatus for automatically inspecting a woven cloth during weaving or a woven cloth for defects.

【0002】[0002]

【従来の技術】従来、織布の外観を検査する場合、カメ
ラにより織布表面の画像を撮像し、その撮像結果から得
られる画像濃淡データをしきい値と比較して外観の異常
を検出する検反装置や、レーザー光を織布に照射し、そ
の反射又は透過光を受光素子にて受光させ、その受光量
のレベルとしきい値を比較して異常を検出する自動検反
方法が知られている。しかし、この検反方法で検知でき
る欠点は、人が目視で簡単に判定できるような糸抜け等
の比較的大きな欠陥に限られる点や振動、外乱等で検知
精度が大きく低下する問題があった。
2. Description of the Related Art Conventionally, when inspecting the appearance of a woven fabric, an image of the surface of the woven fabric is taken by a camera, and image density data obtained from the taken image is compared with a threshold value to detect an abnormality in the appearance. There is known an inspection system or an automatic inspection system that irradiates a woven fabric with a laser beam, receives the reflected or transmitted light by a light receiving element, and compares the level of the received light amount with a threshold to detect an abnormality. ing. However, the disadvantages that can be detected by this inspection method are that they are limited to relatively large defects such as thread breakage that can be easily judged by human eyes, and there is a problem that the detection accuracy is greatly reduced due to vibration, disturbance, and the like. .

【0003】検知精度を向上させる目的で例えば、特開
平4−148852号公報において、光源から織布に照
射されて透過する光を、検査対象の糸方向に配置された
光学スリットを介して受光し、受光波形と基準波形との
比較から異常を検出する方法が開示されている。この方
法は、光を透過する部分、つまり織布開口部の特徴量を
基に欠陥の有無を判定しようとするものであるが、例え
ば、経糸の流れ込み欠陥のように開口部が良品とあまり
変わらない欠陥の場合、検知精度が著しく低下するのは
自明である。又、本手法では織密度が一定でかつ光学ス
リットと検査対象方向の糸が平行であることが前提とな
る。しかしながら、実際の織布の織密度は数多く、その
都度光学スリットの交換が必要となる点や、実際の織上
がりの糸、特に経糸は、側面部で湾曲しており上記条件
は、維持できず検知精度は低下する。
For the purpose of improving the detection accuracy, for example, in Japanese Patent Application Laid-Open No. 4-148852, light transmitted to a woven fabric from a light source is transmitted through an optical slit arranged in the direction of a yarn to be inspected. Discloses a method of detecting an abnormality based on a comparison between a received light waveform and a reference waveform. This method attempts to determine the presence or absence of a defect based on the characteristic amount of the light transmitting portion, that is, the characteristic amount of the woven fabric opening. However, for example, the opening is not much different from a good product such as a warp yarn inflow defect. Obviously, in the case of no defect, the detection accuracy is significantly reduced. Further, in this method, it is assumed that the weaving density is constant and the optical slit and the yarn in the inspection target direction are parallel. However, the weaving density of the actual woven fabric is large, and the point where the optical slit needs to be replaced each time, and the actual woven yarn, particularly the warp, are curved at the side portions and the above conditions cannot be maintained. The detection accuracy decreases.

【0004】又、特開平3−249243号公報におい
て、受光センサーを2対の公知の櫛形とし、両者の出力
の差分値と予め設定されたしきい値との比較から異常を
検出する方法が開示されている。この方法は、2対の櫛
型受光センサーに織布狭領域を2分割した濃淡情報が映
されるため、振動や外乱光が合っても、両者の差分値出
力により相殺される効果がある。しかしながら、本方式
も前記方式と同様に、抽出できる欠陥に制限がある点や
織密度が変わったり、櫛センサーと検査対象の糸の平行
度が維持できないと検知精度は低下する。又両者の提案
は共に、同じセンサーで経糸、緯糸の異常を同時に検知
できない。目視で行っている検査と同等の精度を確保す
るためには、従来の織布開口部の特徴検査に加えて、糸
成分そのものの特徴をも抽出する必要がある。例えば前
述したような経糸流れ込み欠陥のような欠陥に対しては
目視検査では、糸交絡点上の検査対象方向糸の上下関係
の不連続性から欠陥の有無が判る。従ってこれを機械的
に行おうとすると、糸交絡点座標上で経糸が緯糸の上又
は下にある糸成分のみを抽出すれば良いことが判る。こ
れを画像処理にて具現化するためには、濃淡画像の2値
化処理は不可欠であるが、従来の固定2値化法では、緯
糸の上又は下にある経糸成分のみの抽出は対象部の濃度
が均一でなく不可能であった。
Japanese Patent Application Laid-Open No. 3-249243 discloses a method in which two pairs of known light-receiving sensors are used, and an abnormality is detected by comparing a difference between the outputs of the two with a preset threshold value. Have been. In this method, density information obtained by dividing the woven cloth narrow region into two parts is displayed on the two pairs of comb-shaped light receiving sensors, so that even if vibration or disturbance light is combined, there is an effect that the difference value output between the two cancels out. However, in this method, similarly to the above method, the detection accuracy is reduced if there is a limit to the defects that can be extracted, if the weaving density changes, or if the parallelism between the comb sensor and the yarn to be inspected cannot be maintained. In both proposals, the same sensor cannot detect warp and weft anomalies simultaneously. In order to ensure the same accuracy as the inspection performed visually, it is necessary to extract the characteristic of the yarn component itself in addition to the conventional characteristic inspection of the woven fabric opening. For example, for a defect such as a warp inflow defect as described above, in a visual inspection, the presence or absence of the defect can be determined from the discontinuity of the vertical relationship of the inspection target yarn on the yarn interlacing point. Therefore, if this is to be performed mechanically, it is understood that only the yarn component in which the warp is above or below the weft on the coordinates of the yarn interlacing point should be extracted. In order to realize this by image processing, binarization processing of the grayscale image is indispensable. However, in the conventional fixed binarization method, only the warp component above or below the weft is extracted as a target part. Was not uniform and was impossible.

【0005】又、本発明者は上述のごとき問題点を著し
く改善した織布の検反装置を先に提案している。これ
は、投光手段により織布に照射した光をCCD素子にて
撮像し、これによって得られた画像データを基に検査糸
方向の糸ピッチと糸傾斜値とを求め、さらに糸が交差す
る部分の上にある糸成分のみを抽出して2値画像に変換
し、この2値画像内に先に求めた糸情報を基に一対の矩
形領域を自動で設定し、この領域内の2値画像データか
ら統計値を抽出し、この両者の統計値の比較を行うこと
により、織組織の異なる領域を径糸、緯糸の区別なく、
同一光学条件で全幅に対して高精度に検出するものであ
る(特願平7−198171号)。
The inventor of the present invention has previously proposed a woven cloth inspection apparatus which has remarkably improved the problems described above. In this method, light emitted from a light emitting unit onto a woven fabric is imaged by a CCD element, and a yarn pitch and a yarn inclination value in a test yarn direction are obtained based on image data obtained by the CCD device. Only the thread component above the portion is extracted and converted into a binary image, and a pair of rectangular areas are automatically set in the binary image based on the thread information previously obtained, and the binary value in this area is set. By extracting statistical values from the image data and comparing these two statistical values, areas with different woven structures can be distinguished for diameter yarns and weft yarns without distinction.
The detection is performed with high accuracy over the entire width under the same optical conditions (Japanese Patent Application No. Hei 7-198171).

【0006】ここで、上述の本出願人の発明の2値化手
段の特徴は、織布を構成する経糸と緯糸とが交絡する点
で且、検査対象の糸が上に配置される部分のみを自動で
選択して2値画像に変換することで後の欠陥抽出精度を
大幅に向上させたことである。しかしながら、本手段で
は、必要な2値画像のみのデータ生成が全織り条件で完
全でなく、処理に不要な一部の画像データも2値化する
事で欠陥検知精度が低下することが一部の織布で認めら
れた。例えば、照明を散乱照明として、その反射光又は
透過光(この場合、織布の織り密度が低いと仮定)をカ
メラに結像させて撮像させると、糸交差点の画像データ
の濃度値は緯糸の上にある経糸の濃度値と経糸の上にあ
る緯糸の濃度値がほぼ同じ値となり、その結果上記手段
にて2値化した2値画像は例えば、経糸を検査対象とし
た場合、交差点の不要な緯糸成分も一部加わった2値画
像として生成される。その結果、欠陥抽出精度が低下し
ていた。
Here, the above-mentioned binarizing means of the applicant's invention is characterized in that the warp and the weft constituting the woven fabric are entangled, and only the portion where the yarn to be inspected is disposed above. Is automatically selected and converted into a binary image, thereby greatly improving the accuracy of subsequent defect extraction. However, in this means, the data generation of only the necessary binary image is not perfect under all weaving conditions, and some image data unnecessary for processing is binarized, thereby lowering the defect detection accuracy. Woven fabric. For example, if the illumination is scattered illumination and the reflected light or transmitted light (in this case, the woven fabric is assumed to have a low weaving density) is imaged by a camera, and the image is taken, the density value of the image data at the yarn intersection will be The density value of the warp on the upper side and the density value of the weft on the warp become almost the same value. As a result, the binary image binarized by the above-mentioned means is unnecessary for the intersection when the warp is inspected. Such a weft component is also generated as a binary image partially added. As a result, the defect extraction accuracy has been reduced.

【0007】[0007]

【発明が解決しようとする課題】それゆえに、この発明
の課題は、織組織、密度に左右されず、同一光学条件で
経糸異常、緯糸異常を同時に高精度で抽出し、かつ、低
コストで自動的に具現化できる織布の検反装置を供給す
ることである。
SUMMARY OF THE INVENTION Accordingly, an object of the present invention is to simultaneously detect a warp abnormality and a weft abnormality simultaneously under the same optical conditions with high accuracy without being influenced by the weaving structure and the density, and at a low cost, automatically. The purpose of the present invention is to provide a woven cloth inspection device that can be embodied in a specific manner.

【0008】[0008]

【課題を解決するための手段】請求項1に係る発明は、
織布上に該織布の検査方向と直行する方向から該織布面
に対し鋭角に指向性の強い光を照射する投光手段と、前
記織布に対する反射光を結像させて撮像させる撮像手段
と、該撮像手段から撮像された画像データを基に検査方
向糸の織り組織周期と糸傾斜とを自動算出する自動算出
手段と、前記画像データの濃度レベルに応じて全画素デ
ータに対して自動で設定されるしきい値に比べた大小関
係に従って振り分けて2値化する2値化手段と、前記自
動算出手段で求まる情報を基に前記2値化手段にて生成
される2値画像領域内に1対の矩形領域を設定し、該領
域内の画像データから抽出される統計量同士を比較して
その比較結果が正常と見なされるを限界を示すしきい値
を超えた場合に異常と判断する異常判定手段により織布
の検査を行うことを特徴とする。
The invention according to claim 1 is
A light projecting means for irradiating the woven cloth surface with light having high directivity at an acute angle from a direction perpendicular to the inspection direction of the woven cloth, and an imaging for imaging the reflected light on the woven cloth by imaging the woven cloth Means, automatic calculation means for automatically calculating the weaving texture period and yarn inclination of the inspection direction yarn based on the image data imaged from the imaging means, and for all pixel data according to the density level of the image data. A binarizing means for binarizing by binning in accordance with a magnitude relation compared with a threshold value automatically set, and a binary image area generated by the binarizing means based on information obtained by the automatic calculating means A pair of rectangular areas is set within the area, and the statistics extracted from the image data in the area are compared with each other. If the comparison result exceeds a threshold indicating a limit, it is regarded as abnormal. Inspection of the woven fabric by the abnormality judgment means to judge And it features.

【0009】請求項2に係る発明は、請求項1記載の発
明の構成に加え、前記投光手段の照明は、指向性の強い
発光ダイオードを複組個み合わせてモジュール化したこ
とを特徴とする。
According to a second aspect of the present invention, in addition to the configuration of the first aspect, the illumination of the light projecting means is modularized by combining a plurality of sets of light emitting diodes having high directivity. I do.

【0010】請求項3に係る発明は、請求項1記載の発
明の構成に加え、前記投光手段の照明モジュールは、前
記撮像手段と前記織布との間に四方に配置され、検査対
象糸に応じて照射するモジュールを切り替えることを特
徴とする。
According to a third aspect of the present invention, in addition to the configuration of the first aspect, the illumination modules of the light projecting means are arranged on all sides between the imaging means and the woven cloth, and the yarn to be inspected is provided. It is characterized in that the irradiation module is switched according to the condition.

【0011】請求項4に係る発明は、請求項2記載の発
明の構成に加え、前記投光手段に用いられる発光ダイオ
ードの照射開口角度は30°以下であり、前記織布に照
射する照射角度は45°以下であることを特徴とする。
According to a fourth aspect of the present invention, in addition to the configuration of the second aspect, an irradiation opening angle of the light emitting diode used in the light emitting means is 30 ° or less, and an irradiation angle for irradiating the woven fabric is provided. Is 45 ° or less.

【0012】以下に本発明の詳細を説明する。請求項1
記載の本発明によれば、織布上に検査対象糸の方向と直
行方向から鋭角に照射される照投光手段の働きにより、
指向性の強い光が照射される。そして撮像手段の働きに
より、織布からの反射光を集光して撮像される。さらに
自動算出手段の働きにより、撮像された画像データから
検査対象糸の方向、織り組織周期が計算される。さら
に、前記画像データの濃度レベルに応じて全画素データ
に対して自動で設定されるしきい値に比べた大小関係に
従って振り分けて2値化する2値化手段の働きにより2
値化される。そして、前記自動算出手段で求まる情報を
基に前記2値化手段にて生成される2値画像領域内に矩
形の一辺が組織周期サイズである一対の矩形領域を設定
し、両者の領域内の2値画像データから抽出される統計
量同士を比較してその比較結果が正常と見なされる限界
を示すしきい値を超えた場合に異常と判断すことで織布
の検査が行われる。
The details of the present invention will be described below. Claim 1
According to the present invention described, by the function of the illuminating and projecting means that is radiated on the woven fabric at an acute angle from the direction and the direction perpendicular to the direction of the yarn to be inspected,
Light with strong directivity is emitted. Then, the reflected light from the woven fabric is condensed and imaged by the operation of the imaging means. Further, by the operation of the automatic calculating means, the direction of the yarn to be inspected and the weave period are calculated from the captured image data. Further, binarization means for binarizing and binarizing according to a magnitude relation compared with a threshold value automatically set for all pixel data in accordance with the density level of the image data.
Valued. Then, based on the information obtained by the automatic calculation means, a pair of rectangular areas whose one side is the tissue cycle size is set in the binary image area generated by the binarization means. The statistic extracted from the binary image data is compared with each other, and when the comparison result exceeds a threshold value indicating a limit considered to be normal, the woven fabric is inspected by determining that the abnormality is abnormal.

【0013】請求項2に係る発明は、請求項1記載の発
明の構成に加え、前記投光手段の照明は、指向性の強い
発光ダイオードを複数個組み合わせてモジュール化され
る。
According to a second aspect of the present invention, in addition to the configuration of the first aspect, the illumination of the light projecting means is modularized by combining a plurality of light emitting diodes having high directivity.

【0014】請求項3に係る発明は、請求項1記載の発
明の構成に加え、前記投光手段の照明モジュールが、前
記撮像手段と前記織布との間の四方に配置され、切り替
え手段の働きにより、検査対象糸方向と直行する方向の
照明モジュールに切り替えられる。
According to a third aspect of the present invention, in addition to the configuration of the first aspect, the illumination modules of the light projecting means are arranged on four sides between the imaging means and the woven fabric, and By the operation, the illumination module is switched to the illumination module in the direction orthogonal to the direction of the yarn to be inspected.

【0015】請求項4に係る発明は、請求項2記載の発
明の構成に加え、前記投光手段に用いられる発光ダイオ
ードの照射開口角度は30°以下であり、前記織布に照
射する照射角度は45°以下で構成される。以下、本発
明の織布の検反装置の実施の形態を図面に基づいて説明
する。図1はこの発明の一実施例の概略ブロック図であ
る。光源1から照射される光は、織布2上で反射し、カ
メラレンズ3で集光されてカメラ4内のCCD素子に結
像される。光源1は、指向性の強い発光ダイオードを複
数個組み合わせてモジール化して撮像視野と照度を確保
したものである。尚、発光ダイオードの照射開口角度は
30°以下である。さらには、後述する検査対象の糸の
みを強調する効果を高めるために開口角度の小さい発光
ダイオードであることがが好ましい。発光色は、カメラ
4が感度を持つ波長の光であれば特に問題ない。又、織
布に照射する光の強度もCCDの更新周期内で充分な電
荷を蓄積できるレベルであれば特に問題ない。発光ダイ
オードをモージュルにする際の配置は、一列に配置して
も複数列千鳥に配置しても良い。これは、織布の撮像面
積に応じて条件を決めれば良い。但し、発光ダーオード
間のピッチはできる限り狭めて配置するほうが照射面の
照度斑が少なくなる。照明モジュールは単体で配置して
も良いが、カメラ4のケース内に固定し、照射部のみを
開放とした構造がより小型化できるために望ましい。発
光ダイオードの配置の例を図2示す。
According to a fourth aspect of the present invention, in addition to the configuration of the second aspect, the light emitting diode used in the light projecting means has an irradiation opening angle of 30 ° or less, and the irradiation angle for irradiating the woven fabric. Is 45 ° or less. Hereinafter, an embodiment of a woven cloth inspection device of the present invention will be described with reference to the drawings. FIG. 1 is a schematic block diagram of one embodiment of the present invention. Light emitted from the light source 1 is reflected on the woven fabric 2, collected by the camera lens 3, and formed on a CCD element in the camera 4. The light source 1 is a module in which a plurality of light-emitting diodes having high directivity are combined to form a module to secure an imaging field of view and illuminance. The irradiation opening angle of the light emitting diode is 30 ° or less. Further, in order to enhance the effect of emphasizing only the yarn to be inspected, which will be described later, it is preferable that the light emitting diode has a small opening angle. There is no particular problem with the emission color as long as the light has a wavelength at which the camera 4 has sensitivity. There is no particular problem in the intensity of light applied to the woven fabric as long as sufficient charge can be accumulated within the update cycle of the CCD. When the light emitting diodes are arranged in a module, they may be arranged in a single row or in a plurality of rows. The condition may be determined according to the imaging area of the woven fabric. However, when the pitch between the light emitting diodes is set as narrow as possible, the illuminance unevenness on the irradiation surface is reduced. Although the lighting module may be arranged alone, a structure in which the lighting module is fixed in the case of the camera 4 and only the irradiating section is opened is preferable because the size can be further reduced. FIG. 2 shows an example of the arrangement of the light emitting diodes.

【0016】この照明の照射条件は、検査対象糸方向と
直行方向から織布水平面に対して45°以下の角度で照
射する。尚、検査対象糸が変わる場合、切り変え器22
にてカメラ4と織布2との間の四方に設置された照明モ
ジュールの中から検査対象糸方向と直行する方向に配置
されたモジュールに切り替えると良い。これらの照明条
件にて織布に照射することにより、カメラ4には、織布
を構成する経糸と緯糸の交差点の上に配置される検査対
象の糸成分のみを明るく強調した画像として撮像され
る。この効果を現す例を図3に示す。これにより、後述
する画像の2値化処理がより容易となり、その結果、欠
陥検知精度が大幅に向上する。又、本モジュール化によ
り、照明のコストが従来の指向性の強くするためにレン
ズを付与したハロゲンランプ等の照明の約1/50にコ
ストが下がる点や、サイズを大幅に小さくできる点での
効果も大きい。
The illumination condition of this illumination is to irradiate at an angle of 45 ° or less with respect to the horizontal plane of the woven fabric from the direction of the yarn to be inspected and the direction perpendicular to the direction. If the yarn to be inspected changes, the changer 22
It is preferable to switch from among the lighting modules installed on all sides between the camera 4 and the woven fabric 2 to a module arranged in a direction perpendicular to the direction of the yarn to be inspected. By irradiating the woven fabric with these illumination conditions, the camera 4 captures an image in which only the yarn components to be inspected, which are arranged on the intersections of the warp and the weft constituting the woven fabric, are brightly emphasized. . An example showing this effect is shown in FIG. As a result, the binarization processing of an image described later becomes easier, and as a result, the defect detection accuracy is greatly improved. In addition, this modularization reduces the cost of illumination to about 1/50 of that of halogen lamps and the like provided with a lens in order to increase the directivity, and can significantly reduce the size. The effect is great.

【0017】カメラレンズ3の拡大倍率は、製品の織密
度が最も細かなレベルを基準に決定する。一般に、織布
像拡大率の高い画像程、欠陥の抽出が容易な傾向にあ
る。しかし、後述する統計量演算処理領域のサイズは、
織り組織周期に合わせる必要があるために、撮像した画
像内に検査対象方向の糸が少なくとも組織周期の糸数の
倍の本数以上はあることが前提となる。CCDカメラ4
で撮像された濃淡画像データは、A/D変換回路5で8
bitのデジタル画像データに加工された後、フレーム
メモリ6に格納される。格納されたフリーズ画像は、前
処理回路7にて、欠陥を効果的に抽出させるために、微
分強調等の前処理加工を行う。加工された画像から最初
に後述する一対の統計処理用矩形領域のサイズ設定用に
織り組織周期を求める。これは、以下の方式にて実現し
た。矩形領域の短軸は、対象とする糸と垂直方向に配置
される。この幅を最も小さい値(同一光学条件にて撮像
される織布画像で最も検査対象糸密度の高い糸のピッチ
サイズに相当する画素数)から最も大きい値(同一光学
条件にて撮像される織布画像で最も検査対象糸密度の低
い糸のピッチサイズに相当する画素数)まで順に設定し
ながら同じ画像に対して各々のサイズ毎に1 対の比較領
域の画像データから相関値を求める。求まった各々の相
関値の最大値が織り組織周期と一致する事が判った。こ
の値のサイズを比較領域の短軸サイズとした。尚、組織
周期を求める式は、濃淡画像でなく2値画像に変換した
後で行っても良い。又、他に例えば検査対象糸と垂直方
向の濃度波形の特徴追尾から周期性を求める方式やFF
Tの周期性を求める方式でも織り組織の算出は可能であ
るが、方式は特に指定するものではない。
The magnification of the camera lens 3 is determined based on the level at which the weave density of the product is the finest. In general, the higher the woven image magnification ratio is, the easier it is to extract defects. However, the size of the statistic calculation processing area described later is
Since it is necessary to match the weave tissue cycle, it is assumed that the number of yarns in the direction to be inspected is at least twice the number of yarns in the tissue cycle in the captured image. CCD camera 4
The grayscale image data picked up by
After being processed into bit digital image data, it is stored in the frame memory 6. The stored freeze image is subjected to preprocessing such as differential enhancement in a preprocessing circuit 7 in order to effectively extract defects. First, a texture period is determined from the processed image for setting the size of a pair of rectangular areas for statistical processing described later. This was realized by the following method. The short axis of the rectangular area is arranged in a direction perpendicular to the target yarn. From the smallest value (the number of pixels corresponding to the pitch size of the yarn having the highest yarn density to be inspected in the woven fabric image captured under the same optical condition) to the largest value (the fabric image captured under the same optical condition) The correlation value is obtained from the image data of the pair of comparison areas for each size for the same image while sequentially setting up to the number of pixels corresponding to the pitch size of the yarn having the lowest inspection target yarn density in the cloth image. It was found that the maximum value of each obtained correlation value coincided with the weave tissue cycle. The size of this value was taken as the minor axis size of the comparison area. Note that the formula for calculating the tissue cycle may be performed after conversion into a binary image instead of a grayscale image. In addition, for example, a method of obtaining periodicity from characteristic tracking of a density waveform in a direction perpendicular to a yarn to be inspected or an FF
Although the weave structure can be calculated by a method for obtaining the periodicity of T, the method is not particularly specified.

【0018】次に、検査対象方向の平均糸ピッチと平均
糸傾量を自動算出するために濃度投影回路9にて注目糸
方向座標の濃度投影(濃度加算処理)を行い、一次元の
濃度データに加工する。このデータを基にFFT回路8
にてフーリエ変換し、スペクトル最頻値の実数データと
虚数データを求める。このデータから検査方向の糸平均
ピッチ(織密度)、傾き量が求まる。尚、傾きの方向を
求めるために、実際は、上述処理を検査糸方向に対して
画像領域を少なくとも2分割以上設定して行い、得られ
る虚数データつまり位相成分のデータを基に、平均法
や、最小二乗法等ににて一次式を求める。尚、フーリエ
変換は、常時行う必要がないために、CPU17による
ソフト処理でも可能である。又、本方式は、糸抜け等の
欠情報が画像データに含まれる場合でも高精度に求まる
利点がある。
Next, in order to automatically calculate the average yarn pitch and the average yarn inclination amount in the inspection target direction, the density projection circuit 9 performs density projection (density addition processing) of the target thread direction coordinates, and performs one-dimensional density data. Process into Based on this data, the FFT circuit 8
, To obtain real number data and imaginary number data of the spectrum mode. From this data, the average yarn pitch (woven density) and the amount of inclination in the inspection direction are obtained. In addition, in order to obtain the direction of the inclination, in practice, the above-described processing is performed by setting the image area to at least two divisions with respect to the inspection thread direction, and based on the obtained imaginary data, that is, the data of the phase component, A linear equation is obtained by the least square method or the like. Note that the Fourier transform does not need to be performed all the time, and therefore can be performed by software processing by the CPU 17. Further, this method has an advantage that it can be obtained with high accuracy even when missing information such as thread missing is included in the image data.

【0019】糸方向の算出法は、他に例えば、注目糸垂
直方向を微分強調させた後その軸波形のピーク値を追跡
する方法や、公知のHough変換により、糸方向を求
める手法があるが、方式は、特に限定するものではな
い。次に、画像を2値化回路10にて2値化処理を行
う。2値化の目的は、前述の如く、検査方向の糸成分の
みの特徴量を抽出するためである。特に、経糸の場合、
緯糸交絡点上の上又は下にある成分のみの抽出が必要と
なる。これを実現するために、先ず、画像のX軸又は、
Y軸方向の各走査軸濃淡データをフィルタ回路13を通
し、出力値(湾曲波形)をその軸のしきい値とする。同
一処理を全走査軸に対して行い第1の2値画像を生成さ
せる。次に同一の処理をY軸、又はX軸に対して行い、
第2の2値画像を生成させる。若し、第1の2値画像が
経糸方向と垂直方向にフィルタ処理されたものであれ
ば、第1の2値画像は、経糸成分のみの2値画像に、第
2の2値画像は、緯糸成分のみの2値画像に生成され
る。ここで示すフィルタ回路は、予め、検査対象糸方向
の周期性を求め、この周期成分を含む中間周波数領域を
カットしたバンドエリミネーションフィルタ又は、原画
像を平滑化して高周波成分を除去したフィルタである。
尚、上記平滑化の乗数は予め画像データを基に計算され
る密度情報より決定される。このフィルタを通した波形
に若干のオフセット値を加えたしきい値でフィルタ通過
前の画像を2値化処理することにより、第1の2値画像
が経糸成分の像であれば、交絡点上の緯糸の上にある経
糸像のみが、第2の2値画像が緯糸成分の像であれば、
経糸の下にある成分も含めた緯糸成分のみの抽出ができ
る。上述2値化法と上述照明との組み合わせにより、全
織り条件で確実に必要な2値画像のみの抽出が可能とな
った。さらに、照明の変動、照度むら、織布の局所的な
織密むら、糸のつや違い等があっても上記糸情報のみを
確実に抽出できる利点もある。2値化波形の例を図4
に、2値化後の画像の例を図5(a)、(b)に示す。
Other methods of calculating the yarn direction include, for example, a method of differentially emphasizing the vertical direction of the target yarn and then tracking the peak value of the axial waveform, or a method of finding the yarn direction by a known Hough transform. The method is not particularly limited. Next, the image is binarized by the binarization circuit 10. The purpose of the binarization is to extract the characteristic amount of only the yarn component in the inspection direction as described above. Especially in the case of warp,
Only the components above or below the weft interlacing point need to be extracted. To achieve this, first, the X axis of the image or
Each scan axis density data in the Y-axis direction passes through the filter circuit 13, and the output value (curved waveform) is used as the threshold value of the axis. The same process is performed on all scanning axes to generate a first binary image. Next, the same processing is performed on the Y axis or the X axis,
A second binary image is generated. If the first binary image has been filtered in the warp direction and the vertical direction, the first binary image is a binary image of only the warp component, and the second binary image is A binary image of only the weft component is generated. The filter circuit shown here is a band elimination filter in which the periodicity in the yarn direction to be inspected is determined in advance, and an intermediate frequency region including this periodic component is cut, or a filter in which the original image is smoothed and the high-frequency component is removed. .
The multiplier for the smoothing is determined from density information calculated in advance based on the image data. By binarizing the image before passing through the filter with a threshold value obtained by adding a slight offset value to the filtered waveform, if the first binary image is a warp component image, If only the warp image on the weft of the second binary image is the image of the weft component,
Only the weft components including the components under the warp can be extracted. By the combination of the above-described binarization method and the above-mentioned illumination, it is possible to reliably extract only a necessary binary image under all weaving conditions. Furthermore, there is an advantage that only the above-mentioned yarn information can be reliably extracted even if there is a variation in illumination, uneven illuminance, local unevenness in weaving of the woven fabric, a difference in the gloss of the yarn, and the like. FIG. 4 shows an example of a binarized waveform.
FIGS. 5A and 5B show examples of the image after binarization.

【0020】上述第1、第2の2値画面を基に、後述す
る矩形領域処理を行えば、織布の欠陥情報は求まるが、
経糸の2値画像は、経糸が緯糸の上から両隣の緯糸の下
に潜り込む中間の画像も含まれる。この情報は、欠陥抽
出には不必要であるため、経糸検査の場合、第1、第2
画像を論理演算回路14のAND論理演算を行うことに
より、完全な緯糸上のみの経糸画像に変換できる。変換
後の2値画像の例を図5(c)に示す。
If rectangular area processing, which will be described later, is performed based on the first and second binary screens, defect information of the woven cloth can be obtained.
The binary image of the warp also includes an intermediate image in which the warp runs under the weft from above the weft. Since this information is unnecessary for defect extraction, the first and second
By performing an AND logic operation on the image by the logical operation circuit 14, it is possible to convert the image into a warp image only on a complete weft. FIG. 5C shows an example of the converted binary image.

【0021】求まった検査対象方向の組織周期と糸方向
から統計量演算用の矩形領域が自動生成される。これを
平織りを例に図6(a)、(b)に示す。矩形領域の長
軸方向の長さは特に指定するものではないが、糸抜け等
の連続に発生する欠陥に対しては長く設定する程、欠陥
検知の精度は向上する傾向にあり、局所的に発生する毛
羽等の欠陥に対しては短いほうが好ましい。長さは、検
査対象の特徴に合わせて決定すれば良く、計測中に長さ
を可変にして複数回同一処理を行っても良い。矩形領域
の設定法は、図6(a)、(b)に示すような隣接する
一対の形状に限定するものでなく、図6(c)に示すよ
うに、比較する領域を交互に設けても良い。但し、矩形
領域の短軸方向のサイズ(画素数)は、検査対象糸方向
の糸の織り組織周期と一致することが前提である。この
ように設定することで、組織パターンがどのような形態
であろうと同一の検査が可能となる。但し、矩形の長軸
方向と検査対象糸方向との位相がずれると、欠陥の検知
精度は低下する。この問題を回避させるために、上述の
とうり対象糸方向を自動算出させ、矩形領域の主軸方向
を対象糸方向に追従させるか、若しくは画像をその傾き
量だけ回転させ、矩形長軸と対象糸方向に合わせる。こ
れにより、例えば製織中の織布の両側に生生じる縦糸の
傾きやセンサ固定時の軸出しミスによる欠陥検知精度の
低下を回避できる。又、検査対象糸の平均密度を算出
し、統計値算出用矩形領域を自動的に最適化することに
より、織密度の異なる織布を検査する場合でも光学条件
を何等調整することなく同一検査ができる利点がある。
さらに、隣接する矩形領域間の統計値比較処理のために
例えば、検査中に光源の光量が相対的に低下あるいは、
上昇した場合でもそれらの影響は相殺される利点があ
る。但し、ハレーションを起こすような迷い光が近くに
存在する場合は、遮蔽板21を設置すると良い。
A rectangular area for calculating a statistic is automatically generated from the obtained tissue period and the thread direction in the inspection target direction. This is shown in FIGS. 6A and 6B by taking plain weaving as an example. The length of the rectangular area in the major axis direction is not particularly specified. However, as the length is set longer for continuously occurring defects such as thread dropout, the accuracy of defect detection tends to be improved. It is preferable to be short for defects such as fluff that occurs. The length may be determined in accordance with the characteristics of the inspection target, and the same processing may be performed a plurality of times while varying the length during measurement. The method of setting the rectangular area is not limited to a pair of adjacent shapes as shown in FIGS. 6A and 6B, and the areas to be compared are alternately provided as shown in FIG. Is also good. However, it is premised that the size (the number of pixels) of the rectangular area in the short axis direction matches the weaving texture period of the yarn in the inspection target yarn direction. With this setting, the same examination can be performed regardless of the form of the tissue pattern. However, if the phase of the direction of the long axis of the rectangle and the direction of the yarn to be inspected are shifted, the accuracy of detecting a defect is reduced. In order to avoid this problem, the above-described target yarn direction is automatically calculated, and the main axis direction of the rectangular area is made to follow the target yarn direction, or the image is rotated by the amount of inclination, and the rectangular long axis and the target yarn direction are rotated. Adjust to the direction. As a result, for example, it is possible to avoid a decrease in the accuracy of defect detection due to the inclination of the warp occurring on both sides of the woven fabric during weaving and an error in centering when the sensor is fixed. In addition, by calculating the average density of the yarn to be inspected and automatically optimizing the rectangular area for calculating the statistical value, the same inspection can be performed without any adjustment of the optical conditions even when inspecting woven fabrics having different woven densities. There are advantages that can be done.
Furthermore, for statistical value comparison processing between adjacent rectangular areas, for example, the light amount of the light source relatively decreases during the inspection, or
Even if it rises, their effect is offset. However, when stray light that causes halation exists nearby, it is preferable to install the shielding plate 21.

【0022】矩形領域内のラベリングを結合情報統合化
処理回路11にて、各種特徴量の抽出を特徴量抽出回路
12にて行う。矩形領域の統計量としては、黒、又は白
総画素数、総ラベル数と、各ラベル特徴量である面積、
フィレ径、形状比、主軸角、周囲長、近接ラベル重心間
距離等の最大値、平均値、最小値であり、欠陥の抽出
は、上述統計量の差分値、画像パターン相関性、基準デ
ータとの比較から行う。但し、相関性に関しては2値画
像のみでなく元の濃淡画像で行っても良い。欠陥抽出の
例を綾織りをモデルに図7(a)、(b)に示す。図7
(a)は、正常な組織を示し、図7(b)は流れ込み欠
陥の例を示す。このような欠陥は、従来の織布近傍空隙
間の比較では抽出が困難であるが、例に示すように、上
述2値化法による2値画像を上述矩形内で見ると、正常
部と、欠陥部との位置パターン整合性が全く異なる。し
たがって一対の矩形領域間のパターン整合性を演算する
ことで簡単に欠陥が抽出できる事が判った。なお、欠陥
の抽出は、上述統計量を基にCPU17にて行う。演算
結果は、入出力回路20を通して出力される。
The labeling in the rectangular area is performed by the joint information integration processing circuit 11 and the extraction of various feature amounts is performed by the feature amount extraction circuit 12. As the statistics of the rectangular area, the total number of black or white pixels, the total number of labels, and the area that is each label feature amount,
The maximum value, average value, and minimum value of the fillet diameter, shape ratio, principal axis angle, perimeter, distance between adjacent label centroids, etc. From the comparison. However, the correlation may be performed not only with the binary image but also with the original grayscale image. FIGS. 7A and 7B show an example of defect extraction using a twill weave as a model. FIG.
7A shows a normal tissue, and FIG. 7B shows an example of a flow-in defect. Such a defect is difficult to extract by comparison of the conventional voids near the woven cloth, but as shown in the example, when the binary image by the above-described binarization method is viewed in the above-described rectangle, a normal part The positional pattern consistency with the defective part is completely different. Therefore, it was found that a defect can be easily extracted by calculating the pattern consistency between a pair of rectangular regions. The extraction of defects is performed by the CPU 17 on the basis of the above-mentioned statistics. The calculation result is output through the input / output circuit 20.

【0023】製織中の織り上がった織布をインラインで
検査する場合、風綿等の異物が織布表面に付着する。従
来技術で指摘したように、若し、単純に画像の濃度値と
しきい値との比較や、2対の近傍領域内の濃度加算値比
較のみで欠陥を抽出しようとすると、これらの異物を欠
陥と誤判定してしまう。この問題を回避するために、上
述統計量の総合比較、基準値との比較を行う。例えば、
透過方式の検査で、異物がある場合、リード通し違いの
経糸欠点と異物とを上述矩形領域間で比較すると、平均
濃度値や濃度分布形状等は明らかに異なる。従って、従
来の演算方式に加えて、このような統計量の比較演算を
判定に加えることで、欠陥と異物等の外乱要素との分離
ができる。尚、ここに示した判定の際のパラメータとな
る統計量は、特に限定するものでなく、対象欠点で特異
な特徴を示す統計値を予め実験等にて求めるか、インラ
イン中ヒストグラム処理等により求め、それらを組み合
わせて処理すれば良い。又、本方式は、同時に豊富な統
計量の抽出ができるために、例えばファジー推論や重回
帰分析等での欠陥の識別も可能である。
When the woven fabric being woven is inspected in-line, foreign matter such as fly cotton adheres to the surface of the woven fabric. As pointed out in the prior art, if it is attempted to simply extract a defect only by comparing the density value of an image with a threshold value or by comparing density addition values in two pairs of neighboring areas, these foreign substances are detected as defects. Is incorrectly determined. In order to avoid this problem, a comprehensive comparison of the above statistics and a comparison with a reference value are performed. For example,
In the inspection of the transmission system, when there is a foreign matter, when the warp defect in which the lead is passed through and the foreign matter are compared between the above-described rectangular regions, the average density value, the density distribution shape, and the like are clearly different. Therefore, by adding such a statistical comparison operation to the determination in addition to the conventional calculation method, it is possible to separate a defect from a disturbance element such as a foreign matter. The statistic which is a parameter at the time of the determination shown here is not particularly limited, and a statistic indicating a unique characteristic of the target defect is obtained in advance by an experiment or the like, or is obtained by in-line histogram processing or the like. , May be combined and processed. Further, since the present method can simultaneously extract abundant statistics, it is also possible to identify defects by, for example, fuzzy inference or multiple regression analysis.

【0024】図8は本発明の一実施例の動作を説明する
ためのフロチャ−トである。取り込まれた画像の全体を
検査するために、一連の処理が完了された段階で、処理
領域を欠陥抽出に支障のない範囲の画素数だけ検査対象
糸方向と垂直軸にシフトさせて同じ処理を繰り返す経
糸、緯糸異常を同時に検査する場合は、上述一連の処理
理を完了後、検査対象と直行する方向に配置された照明
に切り替えた後、矩形領域を次の対象とする糸方向に合
わせて同じ処理を行う。この場合、比較する特徴量とし
きい値は、前者と同じとは限らない。尚、検査対象が緯
糸の薄段、厚段等である場合、論理演算後の2値画像で
検査を行っても良いが、検査精度を高めるためには、論
理演算前の緯糸成分を抽出した2値画像で行うほうが良
い。尚、全体の制御を行うプログラムは、ROM18に
格納される。織布の全幅を検査する場合、センサーを織
布幅方向にトラバースするかセンサーを複数個、織布幅
方向均一間隔に固定すれば良い。
FIG. 8 is a flowchart for explaining the operation of one embodiment of the present invention. In order to inspect the entire captured image, at the stage when a series of processing is completed, the same processing is performed by shifting the processing area by the number of pixels within the range that does not hinder defect extraction in the direction of the yarn to be inspected and the vertical axis. When simultaneously inspecting for repeated warp and weft abnormalities, after completing the above series of processing, switch to illumination arranged in a direction orthogonal to the inspection target, and then adjust the rectangular area to the next target yarn direction. Performs the same processing. In this case, the feature amount and the threshold to be compared are not necessarily the same as the former. In addition, when the inspection target is a thin section, a thick section, or the like of the weft, the inspection may be performed on the binary image after the logical operation. However, in order to increase the inspection accuracy, the weft component before the logical operation is extracted. It is better to use a binary image. Note that a program for performing overall control is stored in the ROM 18. When inspecting the entire width of the woven fabric, the sensor may be traversed in the woven fabric width direction or a plurality of sensors may be fixed at uniform intervals in the woven fabric width direction.

【0025】上述のごとく、この実施例では製織中の織
布のインライン検査を示したが、当然の如く、織り上が
った織布の自動検反に適用しても良い。又、織布以外の
規則性のある特徴をもったシートであれば、織布以外で
も適用できる。図9は織機に本実施の形態における検反
装置を適用した場合を示す図である。製織中の織布に対
して上から光源1を照射し、CCDカメラ4によって撮
像する。CCDカメラ4は移動軸40に沿って移動可能
となるように取付けられる。
As described above, in this embodiment, the in-line inspection of the woven fabric during weaving is shown. However, the present invention may be applied to the automatic inspection of the woven fabric that has been woven. Further, as long as the sheet has regular characteristics other than the woven cloth, the sheet can be applied to a sheet other than the woven cloth. FIG. 9 is a diagram illustrating a case where the inspection apparatus according to the present embodiment is applied to a loom. The light source 1 is irradiated from above onto the woven fabric being woven, and an image is taken by the CCD camera 4. The CCD camera 4 is mounted so as to be movable along a movement axis 40.

【0026】[0026]

【発明の効果】本本発明によると、従来、検出が困難で
あった欠陥が確実に検出でき、又、織密度が変わった
り、織り組織が変わったり、検査対象の糸方向が変わっ
ても、光学条件を全く変えることなく縦糸、緯糸同時で
且、高精度な、欠陥の抽出が可能となり、又、豊富な抽
出情報により、表面付着物等の外乱要素と欠陥との分
離、さらに、欠陥の識別をもできる装置を提供すること
を可能とした。
According to the present invention, a defect which has been difficult to detect conventionally can be reliably detected, and even if the weaving density changes, the weaving structure changes, or the yarn direction of the inspection object changes, the optical characteristics are improved. It is possible to simultaneously and accurately detect defects in the warp and weft without changing the conditions at all, and to separate defects from disturbance elements such as surface deposits and to identify defects with a wealth of extracted information. It is possible to provide a device that can also perform

【図面の簡単な説明】[Brief description of the drawings]

【図1】 本発明の実施態様における概略ブロック図で
ある。
FIG. 1 is a schematic block diagram according to an embodiment of the present invention.

【図2】 本発明の実施態様における照明モジュールの
配置を説明するための図である。
FIG. 2 is a diagram for explaining an arrangement of a lighting module according to the embodiment of the present invention.

【図3】 本発明の実施態様における照明の効果を説明
するための図である。
FIG. 3 is a diagram for explaining an effect of illumination in the embodiment of the present invention.

【図4】 本発明の実施態様における2値化手法を説明
するためのしきい値の例である。
FIG. 4 is an example of a threshold value for explaining a binarization method according to the embodiment of the present invention.

【図5】 本発明の実施態様における2値化手法及び論
理演算手法を説明するための2値画像の例である。
FIG. 5 is an example of a binary image for explaining a binarization method and a logical operation method according to the embodiment of the present invention.

【図6】 本発明の実施態様における統計量演算用矩形
領域を説明するための例である。
FIG. 6 is an example for explaining a rectangular area for calculating a statistic in the embodiment of the present invention.

【図7】 本発明の実施態様における欠陥抽出法を説明
するための例である。
FIG. 7 is an example for explaining a defect extraction method according to the embodiment of the present invention.

【図8】 本発明の実施態様における動作を説明するた
めのフローチャートである。
FIG. 8 is a flowchart for explaining an operation in the embodiment of the present invention.

【図9】 本発明の実施態様における外観を示す斜視図
である。
FIG. 9 is a perspective view showing an appearance according to an embodiment of the present invention.

【符合の説明】[Description of sign]

1 光源 2 製織中の織布 3 光学レンズ 4 CCDカメラ 5 A/D変換回路 6 フレームメモリ 7 画像前処理回路 8 FFT回路 9 画像濃度投影回路 10 画像2値化回路 11 画像結合情報統合化回路 12 特徴量抽出回路 13 フィルタ回路 14 論理演算回路 15 画像バス 16 CPUバス 17 CPU 18 ROM 19 RAM 20 入出力回路 21 遮蔽板 22 切り替え器 30 一次元/2次元変換器 31 カメラケース 32 透明部材 33 発光ダイオードモジュールケース 40 一軸移動ステージ Reference Signs List 1 light source 2 woven fabric during weaving 3 optical lens 4 CCD camera 5 A / D conversion circuit 6 frame memory 7 image preprocessing circuit 8 FFT circuit 9 image density projection circuit 10 image binarization circuit 11 image integration information integration circuit 12 Feature extraction circuit 13 Filter circuit 14 Logical operation circuit 15 Image bus 16 CPU bus 17 CPU 18 ROM 19 RAM 20 Input / output circuit 21 Shielding plate 22 Switching device 30 One-dimensional / two-dimensional converter 31 Camera case 32 Transparent member 33 Light-emitting diode Module case 40 Uniaxial movement stage

Claims (4)

【特許請求の範囲】[Claims] 【請求項1】 織布上に該織布の検査方向と直行する方
向から該織布面に対し鋭角に指向性の強い光を照射する
投光手段と、前記織布に対する反射光を結像させて撮像
する撮像手段と、該撮像手段から撮像された画像データ
を基に検査方向糸の織り組織周期と糸傾斜とを自動算出
する自動算出手段と、前記画像データの濃度レベルに応
じて全画素データに対して自動で設定されるしきい値に
比べた大小関係に従って振り分けて2値化する2値化手
段と、前記自動算出手段で求まる情報を基に前記2値化
手段にて生成される2値画像領域内に1対の矩形領域を
設定し、該領域内の画像データから抽出される統計量同
士を比較してその比較結果が正常と見なされるを限界を
示すしきい値を超えた場合に異常と判断する異常判定を
手段とを含むことを特徴とする織布の検反装置。
1. A light projecting means for irradiating a light having a high directivity to a woven fabric surface at an acute angle from a direction perpendicular to an inspection direction of the woven fabric on a woven fabric, and forming an image of reflected light on the woven fabric. Imaging means for taking an image by causing the imaging means; automatic calculation means for automatically calculating the weave tissue period and the yarn inclination of the inspection direction yarn based on the image data imaged from the imaging means; A binarizing means for binarizing pixel data in accordance with a magnitude relation compared with a threshold value automatically set, and binarizing means for generating the binary data based on information obtained by the automatic calculating means; A pair of rectangular areas is set in the binary image area, and the statistics extracted from the image data in the area are compared with each other, and the comparison result is considered to be normal. Means for judging abnormalities in the event of Characterized woven cloth inspection device.
【請求項2】 投光手段の照明は、指向性の強い発光ダ
イオードを複数個組み合わせてモジュール化したことを
特徴とする請求項1記載の織布の検反装置。
2. The woven cloth inspection device according to claim 1, wherein the illumination of the light projecting means is modularized by combining a plurality of light emitting diodes having high directivity.
【請求項3】 投光手段の照明モジュ−ルは、撮像手段
と織布との間の四方に配置され、検査対象糸に応じて照
射するモジュ−ル切り替え手段を備えたことを特徴とす
る請求項2記載の織布の検反装置。
3. The lighting module of the light projecting means is provided on four sides between the imaging means and the woven fabric, and includes a module switching means for irradiating according to a yarn to be inspected. The woven cloth inspection device according to claim 2.
【請求項4】 投光手段に用いられる発光ダイオードの
照射開口角度は30゜以下であり、且つ織布に照射する
照射角度は45゜以下であることを特徴とする請求項2
記載の織布の検反装置。
4. The light emitting diode used in the light projecting means has an irradiation opening angle of 30 ° or less, and an irradiation angle of irradiating the woven fabric is 45 ° or less.
A woven cloth inspection device according to the above.
JP9331954A 1997-12-02 1997-12-02 Woven cloth inspection equipment Expired - Fee Related JP3013927B2 (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
JP9331954A JP3013927B2 (en) 1997-12-02 1997-12-02 Woven cloth inspection equipment

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
JP9331954A JP3013927B2 (en) 1997-12-02 1997-12-02 Woven cloth inspection equipment

Publications (2)

Publication Number Publication Date
JPH11172570A true JPH11172570A (en) 1999-06-29
JP3013927B2 JP3013927B2 (en) 2000-02-28

Family

ID=18249505

Family Applications (1)

Application Number Title Priority Date Filing Date
JP9331954A Expired - Fee Related JP3013927B2 (en) 1997-12-02 1997-12-02 Woven cloth inspection equipment

Country Status (1)

Country Link
JP (1) JP3013927B2 (en)

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN103614898A (en) * 2013-11-20 2014-03-05 浙江省纺织测试研究院 Reclaimed fiber fabric judgment method

Families Citing this family (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2020002494A (en) * 2018-06-28 2020-01-09 株式会社豊田自動織機 Stop mark inspection method for loom and stop mark inspection apparatus for loom

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN103614898A (en) * 2013-11-20 2014-03-05 浙江省纺织测试研究院 Reclaimed fiber fabric judgment method
CN103614898B (en) * 2013-11-20 2016-01-27 浙江省纺织测试研究院 A kind of determination methods of reprocessing fiber textile

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