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基于机器视觉的编织袋缺陷在线检测方法

Online Detection Method of Woven Bag Defects Based on Machine Vision

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摘要

针对人工检测编织袋缺陷的正确率低与效率较低的问题,提出一种高效的在线检测编织袋缺陷方法。该方法在线采集编织袋图像并进行图像处理,消除干扰项,准确检测编织袋的缺陷。使用均值滤波器、灰度开闭操作对图像进行预处理,消除图像中干扰缺陷检测的黑白条纹与灰度不均匀,降低噪声。使用差分图像二值化对图像进行背景分割,提取出孔洞缺陷、拉丝缺陷,以及过大的丝线缝隙、褶皱和黑色物。同时,进行开闭运算处理,将断裂的缺陷连接起来并消除过大的丝线缝隙,避免小缺陷的漏检。利用特征提取与缺陷检测消除褶皱和黑色物的干扰,检测出孔洞和拉丝缺陷。实验结果表明,500个试样检测的平均正确检测率达到97.20%,检测效率为720 m/h,检测结果正确率高,效率高。

Abstract

To solve the problem of low accuracy and low efficiency in manual detection of woven bag defects, an efficient online detection method for woven bag defects is proposed. The method collects images of woven bags online and performs image processing to eliminate interference items and accurately detect defects in woven bags. The image is preprocessed by using the mean filter and gray-scale open and close operations to eliminate black and white stripes and gray-scale unevenness that interfere with defect detection in the image, and reduce noise. Use differential image binarization to perform background segmentation on the image, and extract hole defects, wire drawing defects, and excessive wire gaps, wrinkles, and black objects. At the same time, open and close operation is used to connect the broken defects and eliminate the excessive wire gaps in the silk thread, so as to avoid the omission of small defects. Feature extraction and defect detection are used to eliminate the interference of folds and black objects, and detect holes and drawing defects. Experimental results show that the average correct detection rate of 500 samples reaches 97.20%, the detection efficiency is 720 m/h, and the detection accuracy and efficiency are high.

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中图分类号:TP391

DOI:10.3788/LOP57.201507

所属栏目:机器视觉

收稿日期:2020-02-14

修改稿日期:2020-03-06

网络出版日期:2020-10-01

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迟欢:北京科技大学天津学院机械工程系, 天津 301830

联系人作者:迟欢(18644070647@163.com)

【1】Xue Y X, Zhang E H, Wu X Y. The defects detection system of printed products base on computer vision [J]. Packaging Engineering. 2004, 25(5): 185-187.
薛延学, 张二虎, 吴学毅. 基于计算机视觉的印刷包装品缺陷检测系统 [J]. 包装工程. 2004, 25(5): 185-187.
Xue Y X, Zhang E H, Wu X Y. The defects detection system of printed products base on computer vision [J]. Packaging Engineering. 2004, 25(5): 185-187.
薛延学, 张二虎, 吴学毅. 基于计算机视觉的印刷包装品缺陷检测系统 [J]. 包装工程. 2004, 25(5): 185-187.

【2】Mou X G, Cai Y C, Zhou X, et al. On-line yarn cone defects detection system based on machine vision [J]. Journal of Textile Research. 2018, 39(1): 139-145.
牟新刚, 蔡逸超, 周晓, 等. 基于机器视觉的筒子纱缺陷在线检测系统 [J]. 纺织学报. 2018, 39(1): 139-145.
Mou X G, Cai Y C, Zhou X, et al. On-line yarn cone defects detection system based on machine vision [J]. Journal of Textile Research. 2018, 39(1): 139-145.
牟新刚, 蔡逸超, 周晓, 等. 基于机器视觉的筒子纱缺陷在线检测系统 [J]. 纺织学报. 2018, 39(1): 139-145.

【3】Dai B Y, Wu J J. Research of surface defect detection system for stator based on machine vision [J]. Chinese Journal of Sensors and Actuators. 2019, 32(10): 1589-1594.
戴斌宇, 吴静静. 基于机器视觉的定子表面缺陷检测系统研究 [J]. 传感技术学报. 2019, 32(10): 1589-1594.
Dai B Y, Wu J J. Research of surface defect detection system for stator based on machine vision [J]. Chinese Journal of Sensors and Actuators. 2019, 32(10): 1589-1594.
戴斌宇, 吴静静. 基于机器视觉的定子表面缺陷检测系统研究 [J]. 传感技术学报. 2019, 32(10): 1589-1594.

【4】Wang Q C, Jing J F. Horizontal crack detection of metal surface based on machine vision [J]. Journal of Electronic Measurement and Instrument. 2018, 32(11): 71-77.
王清晨, 景军锋. 采用机器视觉的金属表面横向裂纹检测 [J]. 电子测量与仪器学报. 2018, 32(11): 71-77.
Wang Q C, Jing J F. Horizontal crack detection of metal surface based on machine vision [J]. Journal of Electronic Measurement and Instrument. 2018, 32(11): 71-77.
王清晨, 景军锋. 采用机器视觉的金属表面横向裂纹检测 [J]. 电子测量与仪器学报. 2018, 32(11): 71-77.

【5】He Z D, Wang Y N, Liu J, et al. Background differencing-based high-speed rail surface defect image segmentation [J]. Chinese Journal of Scientific Instrument. 2016, 37(3): 640-649.
贺振东, 王耀南, 刘洁, 等. 基于背景差分的高铁钢轨表面缺陷图像分割 [J]. 仪器仪表学报. 2016, 37(3): 640-649.

【6】Lu S Z, Du W L, Chen Z, et al. On-line measuring method of buckwheat hulling efficiency parameters based on machine vision [J]. Transactions of the Chinese Society for Agricultural Machinery. 2019, 50(10): 35-43.
吕少中, 杜文亮, 陈震, 等. 基于机器视觉的荞麦剥壳性能参数在线检测方法 [J]. 农业机械学报. 2019, 50(10): 35-43.

【7】Li D, Bai G J, Jin Y Y, et al. Machine-vision based defect detection algorithm for packaging bags [J]. Laser & Optoelectronics Progress. 2019, 56(9): 091501.
李丹, 白国君, 金媛媛, 等. 基于机器视觉的包装袋缺陷检测算法研究与应用 [J]. 激光与光电子学进展. 2019, 56(9): 091501.

【8】Li M, Sun T B. Research of food packaging defects detection based on machine vision [J]. Food Research and Development. 2016, 37(24): 125-127.
李萌, 孙铁波. 基于机器视觉的食品包装缺陷检测研究 [J]. 食品研究与开发. 2016, 37(24): 125-127.

【9】Jia Z Z, Zhang T, Cao X Q, et al. Design and realization of the food inner packaging detection device based on the machine vision [J]. Food and Machinery. 2018, 34(7): 111-114.
贾真真, 张涛, 曹兴强, 等. 基于机器视觉的食品内包装缺陷检测装置设计与实现 [J]. 食品与机械. 2018, 34(7): 111-114.

【10】Chen H L, Li J W. Detection system design of instant noodle packaging quality based on machine vision [J]. Packaging Engineering. 2017, 38(13): 159-163.
陈慧丽, 李继伟. 基于机器视觉的方便面包装品质检测系统设计 [J]. 包装工程. 2017, 38(13): 159-163.

【11】Li Z J. Automatic detection system for drug packaging line based on machine vision [J]. Packaging Engineering. 2018, 39(17): 165-169.
李姿景. 基于机器视觉的药品包装生产线自动检测系统 [J]. 包装工程. 2018, 39(17): 165-169.
Li Z J. Automatic detection system for drug packaging line based on machine vision [J]. Packaging Engineering. 2018, 39(17): 165-169.
李姿景. 基于机器视觉的药品包装生产线自动检测系统 [J]. 包装工程. 2018, 39(17): 165-169.

【12】Li M, Sun T B. Design of machine vision based aluminum-plastic drug packaging on-line detection system [J]. China Plastics Industry. 2016, 44(4): 138-141.
李萌, 孙铁波. 基于机器视觉的铝塑药品包装在线检测系统 [J]. 塑料工业. 2016, 44(4): 138-141.

【13】Chen W H, Zhang J, Fan Y Y, et al. A method based on background subtraction and frame difference algorithm for moving target detection [J]. Electronic Design Engineering. 2013, 21(3): 24-26.
陈文会, 张晶, 樊养余, 等. 一种基于背景减法和帧差的运动目标检测算法 [J]. 电子设计工程. 2013, 21(3): 24-26.

引用该论文

Chi Huan. Online Detection Method of Woven Bag Defects Based on Machine Vision[J]. Laser & Optoelectronics Progress, 2020, 57(20): 201507

迟欢. 基于机器视觉的编织袋缺陷在线检测方法[J]. 激光与光电子学进展, 2020, 57(20): 201507

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