AUTHOR=Jin Haibo , Li Mengjiao , Huang Chunxiao , Peng Jishen 

TITLE=LSA-YOLO: a bearing surface defect detection method based on low-order response aggregation and progressive attention

JOURNAL=Frontiers in Physics

VOLUME=Volume 13 - 2025

YEAR=2025

URL=https://www.frontiersin.org/journals/physics/articles/10.3389/fphy.2025.1722962

DOI=10.3389/fphy.2025.1722962

ISSN=2296-424X

ABSTRACT=Bearing defect detection is crucial for equipment safety and maintenance costs, but challenges remain under complex textured backgrounds, reflective stains, and irregular defect shapes. This paper presents the LSA-YOLO method for industrial field applications, which strengthens detail retention through low-order feature aggregation, improves irregular defect representation through multi-scale residual modeling, and enhances anti-interference ability via a progressive spatial attention mechanism, without the need for additional annotations or complex post-processing. Experimental results on a bearing surface defect dataset show that LSA-YOLO achieves a good balance between precision and efficiency, with an F1 score of 88.1% and mAP@0.5 of 92.6%, significantly outperforming the baseline model. This method is suitable for online quality inspection scenarios, and relevant training details and limitations are discussed in the paper.