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Open Access

ARTICLE

SIM-Net: A Multi-Scale Attention-Guided Deep Learning Framework for High-Precision PCB Defect Detection

Ping Fang, Mengjun Tong*
Mathematics and Computer Science College, Zhejiang Agriculture and Forestry University, No. 666 Wusu Street, Lin’an District, Hangzhou, 311300, China
* Corresponding Author: Mengjun Tong. Email: email

Computers, Materials & Continua https://doi.org/10.32604/cmc.2025.073272

Received 14 September 2025; Accepted 10 December 2025; Published online 30 December 2025

Abstract

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.

Keywords

Deep learning; small object detection; PCB defect detection; attention mechanism; multi-scale fusion network

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