基于机器学习的金属软管缺陷检测系统

Machine learning based defect detection system for metal hoses

为了实现工业上对金属软管缺陷部分的自动检测,提出一种基于深度学习的缺陷检测方法,首先利用相机采集金属软管缺陷部分的图像并将采集图像中的缺陷特征部分进行分类与标定,金属软管外表面缺陷可分为断丝、散丝、叠丝3种并制作出对应的自制数据集;其次对YOLOv5s网络进行改进,通过在YOLOv5s中的主干网络中添加SimAM注意力机制;然后利用EIoU损失函数替换初始网络所采用的IoU损失函数;最后对YOLOv5s中的金字塔池化层进行改进,采用SimSPPF模块替换SPPF模块。利用改进后的算法对金属软管缺陷数据集进行训练,改进后的算法相较于初始YOLOv5s网络的平均精度mAP提升了1.5%,特征复杂且小目标的漏检误检情况有了明显改善。

In order to achieve automatic detection of defects in metal hoses in industry,a deep learning based defect detection method is proposed.Firstly,a camera is used to capture images of defects in metal hoses,and the defect feature parts in the collected images are classified and calibrated.The surface defects of metal hoses can be divided into three types:broken wire,loose wire,and stacked wire,and corresponding self-made datasets are created;Secondly,the YOLOv5s network is improved by adding SimAM attention mechanism to the backbone network of YOLOv5s;Then use the EIoU loss function to replace the IoU loss function used by the initial network;Finally,the pyramid pooling layer in YOLOv5s was improved by replacing the SPPF module with the SimSPPF module.The improved algorithm was used to train the dataset of metal hose defects.Compared with the initial YOLOv5s network,the average accuracy mAP of the improved algorithm increased by 1.5%,and the missed and false detections of complex features and small targets were significantly improved.