摘要
为提高工业上焊缝缺陷自动检测与处理的效率,基于深度学习提出一种改进的YOLOv5焊缝缺陷检测方法。针对焊缝样本数据不足的问题,提出一种Mosaic+Mixup的数据增强策略,同时为减少网络的计算量和网络参数量,引入轻量型的GhostNet网络代替YOLOv5主干网络中CSP1模块中的残差模块,并且采用CIOU_Loss作为坐标位置损失提高算法的收敛速度与准确率。最后使用测试集进行焊缝缺陷检测,改进的YOLOv5的平均精度均值(mAP)达到96.88%,单张图片检测时间不超过50 ms,优于传统机器学习算法,能够满足实际工程中对焊缝缺陷的实时性检测要求。
An improved YOLOv5 weld defect detection method based on deep learning was proposed in response to improve the efficiency of automatic detection and processing of weld defects in industry. Aiming at the insufficient weld sample data, a mosaic+mixup data augmentation strategy was proposed. At the same time, in order to reduce the amount of calculation and parameters of network, a lightweight GhostNet network was introduced to replace the residual module in CSP1 module in YOLOv5 backbone network, and CIOU_Loss was used as the coordinate position loss to improve the convergence rate and accuracy of the algorithm. Finally, the testing set was used for weld defect detection. The improved YOLOv5 has a mean average precision(mAP) of 96.88%, and the detection time of a single image is no more than 50 milliseconds, which is better than the traditional machine learning algorithms, could meet the real-time detection requirements of weld defects in practical engineering.
作者
李衍照
于镭
田金文
Li Yanzhao;Yu Lei;Tian Jinwen(College of Automation and Electronic Engineering,Qingdao University of Science and Technology,Qingdao 266061,China)
出处
《电子测量技术》
北大核心
2022年第19期70-75,共6页
Electronic Measurement Technology
基金
山东省重大科技创新工程(2021SFGC0601)项目资助。
