Research on the PCB Defect Detection Algorithm Based on YOLOv5
Aiming at the problems that the defect objects in PCB are small,have many types,and are difficult to identify,an improved YOLOv5 algorithm is proposed to realize the effective treatment of this problem.Firstly,SEnet was introduced to automatically extract the importance of each feature channel through learning,and the accuracy of object detection was improved.Then,the decoupling head idea is introduced into the YOLOv5 network to improve the accuracy of fault detection and accelerate the convergence of the network.The experimental results show that the improved YOLOv5 algorithm's mAP@5 value reaches 92.5％,and the mAP@0.5:0.95 value reaches 47.5％,which are 4.3％and 2％higher than the original ones.In addition,the accuracy of each type of defect is significantly improved,which proves the effectiveness of the algorithm.
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维普
