A High-Performance and Enhanced Generalization Small Target Defect Detection Method for PCB Boards Based on YOLO-EMAC
Han Wu, Yunhan Lin
Abstract
Defect detection in printed circuit boards (PCBs) is essential for ensuring the stability and reliability of electronic equipment. However, due to the compact structures and complex backgrounds of PCBs, accurately detecting small defects while maintaining strong model generalization remains a significant challenge. To address this issue, this paper proposes YOLO-Efficient-Multiscale-Adaptive-Channel (YOLO-EMAC), a novel framework built upon an enhanced YOLOv12 architecture, designed to improve the accuracy, efficiency, and generalization of small defect detection. Specifically, to mitigate the loss of shallow features caused by extensive downsampling and convolutions, the Multi-scale and Multi-branch Cross-scale Convolutional Fusion (M2C2f) module is introduced, integrating Window-based and Multi-Head Self-Attention (WindowMHSA) with dynamic transformation layers to enhance feature extraction for small objects. Furthermore, to address feature degradation during upsampling, a Efficient-Adaptive-Multibranch-Channel (EAMC) module is developed to adaptively strengthen key feature. Extensive experiments conducted on the PCB Defect Dataset, PCB Surface Defect Dataset, and DeepPCB dataset demonstrate that YOLO-EMAC achieves SOTA detection performance. The source code, pretrained models and datasets are made publicly available on GitHub: https://github.com/wuhan66/YOLO-EMAC/tree/main.