YOLO-J based PCB defect detection algorithm

Aiming at the problems of low accuracy and excessive number of model parameters in existing Printed Circuit Boards (PCB) defect detection methods, a PCB defect detection algorithm based on YOLO-J with improved YOLOv4 was proposed. To solve the problern that CSPDarknet53 in YOLOv4 has too many parameters to deploy on mobile, the Resnet50 was used as the feature extraction network for the model. To avoid reducing the detection effect by replacing the feature extraction network, the feature extraction capability of the model for small target PCB defects was enhanced by adding the attention mechanism and improving the PANet structure. The H-Swish activa-tion function was used as the activation function of the neck for the purpose of improving detection accuracy and training speed. In addition, to solve the problem that the initial anchor frame was not suitable for detecting PCB defects, bisecting K-means was used to Cluster the PCB dataset. The PCB defect dataset published by Peking University Laboratory was used for the experiment. The results showed that compared with YOLOv4, the mAP of the proposed method increased by 0. 29% when IOU: 0. 5; when IOU: 0. 5:0. 95, both mAP and recall increased by 6. 7% and the speed increased by 2. 24FPS, and the model size was 132MB, which was about 1/2 of YOLOv4. © 2024 CIMS. All rights reserved.