A PCB Defect Detector Based on Coordinate Feature Refinement

Accurate and efficient detection of printed circuit board (PCB) defects is essential for the reliability and yield of electronic products. However, the PCB defects are generally too tiny to be effectively identified by existing object detection models. In this article, a novel detection network for PCB defect detection is proposed based on the coordinate feature refinement (CFR) method. The CFR structure is designed to suppress the conflicting information from different levels in order to highlight the PCB defect features. Then, four CFR modules are combined with the YOLOv5s baseline framework whose network structure is further optimized by utilizing content-aware reassembly of features (CARAFE) upsampler to aggregate contextual semantic information in large receptive fields, and by integrating an additional lager detection layer to strengthen the detection for small targets. Compared with several state-of-the-art detection models, the proposed detector exhibits significant advantage in detection accuracy of PCB defects with fairly compact model size, and provides a feasible solution to fulfill the industrial requirement of real-time PCB defect detection.