APMLF-YOLO: a progressive multi-level feature fusion algorithm for industrial PCB tiny defect detection - Wang, Shoubin; Li, Kun; Peng, Guili; Gao, Pengcheng; Xing, Zhihan; Zhang, Hanrui; Gao, Zimeng; Li, Youbing; Fang, Xinchang; Jing, Lewei (Signal, image and video processing, 2025)
The Printed Circuit Board (PCB) relies on manual and electrical detection methods for quality inspection in its early stages. Traditional methods not only fail to meet the requirements of modern industry for detection efficiency and accuracy, but also make it difficult to achieve industrial deployment. Therefore, this paper proposes a high-precision defect detection model called the attention and multi-level feature fusion YOLO algorithm (APMLF-YOLO). The purpose of its design is to achieve high-efficiency and high-precision detection of PCBs. We redesign the Neck structure of the model and propose a multi-level feature fusion network module with multi-angle parallel processing. By integrating the MHSA mechanism into the AFPN structure and further adding a small object detection layer, the problem of multi-scale feature fusion is effectively addressed, enhancing the model’s perception of subtle defects. Subsequently, we designed a lightweight salient feature extraction module called CAM-Light to integrate into the backbone structure of the network, enabling the model to more effectively focus on defect areas. Finally, a sliding loss function is introduced to learn the importance of different input features. The method presented in this article demonstrates significant detection performance on the PCB dataset: The mAP50 achieves 99.1% with a 3.3% improvement, while mAP50-95 improved by 6.4%, the F1 score increased by 3%, and the number of model parameters is only 5.1 M. This model shows excellent robustness and comprehensive performance in small and complex defect detection tasks, making it more suitable for industrial applications.