文献类型：期刊文献

英文题名：Surface Defect Detection of Industrial Parts Based on YOLOv5

作者：Le, Hai Feng[1];Zhang, Lu Jia[2];Liu, Yan Xia[3]

第一作者：Le, Hai Feng

通讯作者：Liu, YX[1]

机构：[1]Beijing Union Univ, Beijing Key Lab Informat Serv Engn, Beijing, Peoples R China;[2]Beijing Union Univ, Beijing Key Lab Informat Serv Engn, Beijing, Peoples R China;[3]Beijing Union Univ, Coll Urban Rail Transit & Logist, Beijing, Peoples R China

第一机构：北京联合大学北京市信息服务工程重点实验室

通讯机构：[1]corresponding author), Beijing Union Univ, Coll Urban Rail Transit & Logist, Beijing, Peoples R China.|[11417]北京联合大学;

年份：2022

卷号：10

起止页码：130784-130794

外文期刊名：IEEE ACCESS

收录：;EI(收录号：20230113325751);WOS:【SCI-EXPANDED(收录号:WOS:000902063800001)】；

基金：This work was supported in part by the Science and Technology Program of Beijing Municipal Education Commission under Grant KM201911417007, and in part by the key Program of Beijing Union University for Educational Reform under Grant JY2021Z002

语种：英文

外文关键词：Defect detection; YOLOv5; transformer; deep learning; fine-grained detection

摘要：Industrial product quality inspection, a crucial procedure in industrial production, is crucial in assuring product yield. Product safety and quality inspections on industrial assembly lines are predominantly manual, and there is currently a dearth of safe and dependable inspection techniques. An improved surface defect detection approach based on YOLOv5 is proposed for the problem of surface flaws in industrial components in order to improve the quality detection effect of industrial production parts. To improve the effect of dense object detection, the image features are extracted by the convolutional network and enhanced by coordinate attention. BiFPN is utilized to fuse multi-scale features in order to lower the rate of missed detection and false detection for small target samples. The detectors from the Transformer structure are added to the complex problem of fine-grained detection to improve the predictability of challenging occurrences. According to the experimental findings, on the dataset for industrial parts defects, the proposed network increases the recall of the original algorithm in abnormal classes by 5.3%, reaching 91.6%. Its inference speed can approach 95FPS, indicating an improved real-time detection performance.