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

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Titel: SIM-Net: A Multi-Scale Attention-Guided Deep Learning Framework for High-Precision PCB Defect Detection.
Autoren: Fang, Ping, Tong, Mengjun
Quelle: Computers, Materials & Continua; 2026, Vol. 87 Issue 1, p1-17, 17p
Schlagwörter: DEEP learning, OBJECT recognition (Computer vision), DEFECT tracking (Computer software development), ARTIFICIAL neural networks
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. [ABSTRACT FROM AUTHOR]
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Datenbank: Complementary Index
Beschreibung
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. [ABSTRACT FROM AUTHOR]
ISSN:15462218
DOI:10.32604/cmc.2025.073272