Paper Publications

Impact Factor:6.6

DOI number:10.1016/j.asoc.2025.114072

Journal:Applied Soft Computing

Key Words:AI in engineering; Defect detection; Spatial attention mechanism; Deep learning; Computer vision; Transformer

Abstract:The integrity of Printed Circuit Boards (PCBs) is critical to the performance and reliability of electronic devices. However, existing deep learning-based detection methods often struggle to accurately identify small and complex defects in challenging industrial environments, primarily due to their inability to effectively model irregular defect morphologies, sensitivity to background noise, insufficient multi-scale feature fusion, and difficulties in achieving efficient detection under limited computational resources. To address these issues, we propose a novel object detection framework based on YOLOv8, which integrates a dual-branch feature extraction module, a deformable attention mechanism, and an enhanced spatial attention head. Specifically, we design a Dual-Transformer downsampling module that effectively captures both global context and local details of PCB defects. We also introduce a deformable attention mechanism into the C2f module to handle irregular defect shapes adaptively. Furthermore, we propose a lightweight detection head that employs multi-scale spatial attention and depthwise separable convolutions to enhance feature representation while reducing computational cost. To improve the localization accuracy of small defects, we introduce a new loss function that combines Normalized Wasserstein Distance with Wise-IoUv3. Extensive experiments on the publicly available PKU-Market-PCB dataset demonstrate that YOLO-DTS achieves a precision of 88.4%, a recall of 69.9%, and an mAP50 of 77.5%, outperforming the baseline YOLOv8 by 4.9%, 7.5%, and 7.5%, respectively. The parameters used have been reduced by 13.4% compared to the baseline model. Additional experiments on DeepPCB and aluminum profile defect datasets further validate the strong generalization capability of our method. The results indicate that YOLO-DTS is a robust and efficient solution for PCB defect detection in real-world industrial scenarios.

Indexed by:Journal paper

Discipline:Engineering

Document Type:J

Volume:186

Issue:A

Page Number:114072

ISSN No.:1568-4946

Translation or Not:no

Date of Publication:2025-10-21

Included Journals:SCI

Links to published journals:https://www.sciencedirect.com/science/article/pii/S1568494625013857

First Author:Yufeng Ou

Correspondence Author:Chia-Hung Wang