Work

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

Year: 2025

Type: article

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 [email protected] 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.

Source: Frontiers in Physics

DOI: 10.3389/fphy.2025.1722962

Authors: Haibo Jin, Mengjiao Li, Chunxiao Huang, Jishen Peng

Institution: Liaoning Technical University

Language: English

Topic: Industrial Vision Systems and Defect Detection

Subfield: Industrial and Manufacturing Engineering

Field: Engineering

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $3,077

Funders: National Natural Science Foundation of China, Natural Science Foundation of Liaoning Province