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智能制造21年9期

基于 YOLO v5 算法的板材质量视觉检测技术研究
刘风华 ¹,谢国贤 ¹,肖浩南 ²,杨良生 ¹,黎剑锋 ¹
(1. 广州弘亚数控机械股份有限公司,广东 广州 510535;2. 广州王石软件技术有限公司,广东 广州 510535)

摘  要:对于家具板件自动化生产,快速、准确的板件质量检测系统是不可或缺的。以 YOLO v5 算法为基础对计算机辅助检查板件质量技术进行了研究。在机器学习算法的基础上,结合运动目标捕捉算法作为辅助检测方法,实现板件的识别和定位,然后通过 OpenCV 中的图像处理和轮廓识别方法来实现板件尺寸测量。该算法的板件识别率达 98.33%,识别多种颜色板件外形长宽尺寸时,实测最大尺寸误差 2.23 mm,能满足家具板件生产质量检测的需求。


关键词:机器视觉;YOLO v5;边缘检测;尺寸检测



DOI:10.19850/j.cnki.2096-4706.2021.09.039


基金项目:广东省科技计划项目(2018A050 506024)


中图分类号:TP277                                        文献标识码:A                                   文章编号:2096-4706(2021)09-0149-05


Research on Visual Inspection Technology of Plate Quality Based on YOLO v5 Algorithm

LIU Fenghua1 ,XIE Guoxian2 ,XIAO Haonan2 ,YANG Liangsheng1 ,LI Jianfeng1

(1.Guangzhou KDT Machinery Co.,Ltd.,Guangzhou 510535,China; 2.Guangzhou Wangshi Software Technology Co.,Ltd.,Guangzhou 510535,China)

Abstract:For the automatic production of furniture panel,fast and accurate panel quality inspection system is indispensable. Based on YOLO v5 algorithm,the technology of computer-aided inspection of plate quality is studied. On the basis of machine learning algorithm,combined with the moving object capture algorithm as an auxiliary detection method,the panel recognition and location were realized. Then the panel size measurement is realized by image processing and contour recognition in OpenCV. The recognition rate of the algorithm is 98.33%. When recognizing the length and width dimensions of multi-color panels,the measured maximum dimension error is 2.23 mm,which can meet the needs of furniture panel production quality detection.

Keywords:machine vision;YOLO v5;edge detection;size detection


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作者简介:刘风华(1968—),男,汉族,北京人,副高级工 程师,硕士,研究方向:家具智能制造。