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

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.