You-Only-Look-Once Multiple-Strategy Printed Circuit Board Defect Detection Model

Addressing the challenges of complex backgrounds, minute defects, and irregular shapes in PCB defect images that often lead to missed detections, inaccurate localizations, and false positives, this article introduces an improved you only look once (YOLO) model, termed YOLO-Biformer, to enhance the network’s ability to detect surface defects on PCBs. First, YOLO-Biformer incorporates a hybrid attention module to differentiate the importance among various channels, thus strengthening the extraction of small target defect features and preventing the loss of minor target information often caused by deep convolutional networks. Second, the model introduces a jumping hollow space convolutional pyramid aimed at preserving more image details and interrelated information, thereby boosting the network’s defect localization capability. Finally, the Enhanced Intersection Over Union-FocalLoss loss function is employed to enhance the network’s ability to distinguish between defects with similar features. Experimental results demonstrate that the proposed algorithm increases the mean average precision by 4.1%, showing excellent performance in recognizing small target defects on printed circuit board surfaces and achieving both high accuracy and real-time capabilities.