计算机工程与应用2024，Vol.60Issue(5)：321-327,7.DOI:10.3778/j.issn.1002-8331.2306-0425
改进YOLOv5的汽车齿轮配件表面缺陷检测
Improved YOLOv5 Model for Surface Defect Detection of Automotive Gear Components
摘要
Abstract
Aiming at the problems of low efficiency and poor precision in surface defect detection of automotive gear components,an improved defect detection method YOLO-CNF based on YOLOv5 is proposed.Firstly,add the CBAM attention module to the backbone network to make the model pay more attention to the defect areas of gear components and improve the ability to identify small defects.Secondly,the F2C module is designed to fuse shallow features,which alleviates the problem of the loss of small defect location information to a certain extent.Finally,NWD is used to opti-mize the regression loss to reduce the sensitivity to small target position deviations,and further improving the accuracy and precision of target positions.The experimental results show that the average precision of the improved algorithm reaches 86.7% ,which is 3.2 percentage points higher than the original algorithm,and the detection speed is 43 frames per second.The improved algorithm basically meets the needs of the surface defect detection of automotive gear components.关键词
缺陷检测/齿轮配件/CBAM/特征融合/NWD距离Key words
defect detection/gear components/convolutional block attention network(CBAM)/fuse features/normalized Wasserstein distance(NWD)分类
信息技术与安全科学引用本文复制引用
朱德平,程光,姚景丽..改进YOLOv5的汽车齿轮配件表面缺陷检测[J].计算机工程与应用,2024,60(5):321-327,7.基金项目
国家重点研发计划(2021YFB1715700). （2021YFB1715700）
