TY  - EJOU
AU  - Fang, Ping 
AU  - Tong, Mengjun 

TI  - SIM-Net: A Multi-Scale Attention-Guided Deep Learning Framework for High-Precision PCB Defect Detection
T2  - Computers, Materials \& Continua

PY  - 2026
VL  - 87
IS  - 1
SN  - 1546-2226

AB  - Defect detection in printed circuit boards (PCB) remains challenging due to the difficulty of identifying small-scale defects, the inefficiency of conventional approaches, and the interference from complex backgrounds. To address these issues, this paper proposes SIM-Net, an enhanced detection framework derived from YOLOv11. The model integrates SPDConv to preserve fine-grained features for small object detection, introduces a novel convolutional partial attention module (C2PAM) to suppress redundant background information and highlight salient regions, and employs a multi-scale fusion network (MFN) with a multi-grain contextual module (MGCT) to strengthen contextual representation and accelerate inference. Experimental evaluations demonstrate that SIM-Net achieves 92.4% mAP, 92% accuracy, and 89.4% recall with an inference speed of 75.1 FPS, outperforming existing state-of-the-art methods. These results confirm the robustness and real-time applicability of SIM-Net for PCB defect inspection.
KW  - Deep learning; small object detection; PCB defect detection; attention mechanism; multi-scale fusion network

DO  - 10.32604/cmc.2025.073272