Defect-Aware Hybrid Attention and Anchor Optimization for Automated PCB Defect Detection in Industrial Inspection

This work proposes a defect-aware Faster R-CNN framework that combines residual CBAM feature refinement with dataset-informed anchor configuration for small PCB defect detection. The model achieved 95.57% mAP@0.5 and 95.59% F1-score while operating at 23 FPS, demonstrating a practical balance between detection accuracy and inference efficiency.