陈世彬,唐英杰,赵鹏.复杂底纹柔性卫生用品表面缺陷检测算法研究[J].包装工程,2018,39(23):132-137.
CHEN Shi-bin,TANG Ying-jie,ZHAO Peng.Surface Defect Detection Algorithm for Flexible Hygiene Products with Complex Shading[J].Packaging Engineering,2018,39(23):132-137. |
| 复杂底纹柔性卫生用品表面缺陷检测算法研究 |
| Surface Defect Detection Algorithm for Flexible Hygiene Products with Complex Shading |
| 投稿时间：2018-08-07 修订日期：2018-12-10 |
| DOI：10.19554/j.cnki.1001-3563.2018.23.023 |
| 中文关键词: 柔性卫生用品 机器视觉 Halcon 图像检测 |
| 英文关键词: flexible hygiene products machine vision Halcon image detection |
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| 中文摘要: |
| 目的 柔性卫生用品由于其材料表面不平整、产品容易发生变形等特性，给机器视觉中图像关键区域的定位和分割带来了很大问题，从而影响检测结果。为了解决复杂底纹柔性卫生用品表面脏点的检测问题，提出一种新型检测算法，以实现对复杂底纹柔性卫生用品快速又精准的检测。方法 在该检测算法中首先对采集的模板图像和样本图像进行预处理,在模板图像中创建一个合适特征模板，根据特征模板进行样本图像的匹配和定位；然后把经过精准定位后的样本图像进行关键区域分割；最后运用差影法把样本关键区域中的缺陷找出。该算法采用德国MVtec公司的halcon算子加以编程实现。结果 实验结果显示，该算法能够识别棉芯表面大于0.04 mm2的脏点，检测1幅图片的平均时间平均在100 ms，检测准确率为100%。结论 文中方法与传统方法相比，鲁棒性强，速度快，能够满足工业高速生产的需求。 |
| 英文摘要: |
| Uneven material surface, likeliness to deform and other features of the flexible hygiene products bring great problems to the positioning and segmentation of key areas of the image in machine vision, thus affecting the detection results. In order to solve the problem of detecting dirty spots on complex shading flexible hygiene products, the work aims to propose a new detection algorithm to realize the rapid and accurate detection of complex shading flexible hygiene products. Firstly, the proposed detection algorithm pre-processed the collected template images and sample images, created a proper feature template from template images, matched and located sample images according to feature templates. Then, the key areas of the accurately located sample images were segmented. Finally, the difference shadow method was used to find out the defects in the key region of the sample. The proposed algorithm was implemented by halcon operator of MVtec company in Germany. Experimental result showed that the proposed algorithm could identify dirty spots greater than 0.04 mm2 on the surface of the cotton core. The average time cost was 100 ms, and the detection accuracy was 100%. Compared with traditional methods, the proposed method is robust, fast and able to meet the needs of industrial high-speed production. |
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