Journal of Guangxi Normal University(Natural Science Edition) ›› 2025, Vol. 43 ›› Issue (6): 80-91.doi: 10.16088/j.issn.1001-6600.2024121901

• Intelligence Information Processing • Previous Articles     Next Articles

Lightweight Bearing Defect Detection Algorithm Based on SBSI-YOLO11

WEI Zishu1, CHEN Zhigang1,2*, WANG Yanxue1, Hasitieer Madetihan1   

  1. 1. School of Mechanical-Electronic and Vehicle Engineering, Beijing University of Civil Engineering and Architecture, Beijing 100044, China;
    2. Beijing Construction Safety Monitoring Engineering Technology Research Center, Beijing 100044, China
  • Received:2024-12-19 Revised:2025-04-21 Published:2025-11-19

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Abstract: In order to solve the problems of low detection accuracy, large model parameters and difficult detection of small targets in the detection of bearing appearance defects in the existing deep learning models, a lightweight detection algorithm SBSI-YOLO11 is proposed. Firstly, the SPD-Conv (space-to-depth convolution) module is introduced into the backbone network to reduce the resolution of the feature map, enhance the feature extraction and reduce the number of parameters of the model. Secondly, the Bidirectional Feature Pyramid Network (BiFPN) and the Attention Mechanism (SGE) are introduced in the Neck part to improve the detection performance of the model for small targets. Finally, the Inner-CIoU loss function is introduced to improve the positioning ability of the model. Experimental results show that compared with the basic model, the SBSI-YOLO11 model shows good comprehensive detection performance, with an mAP of 90.4%, an increase of 2.9 percentage points, a decrease of 11.5% in the amount of parameters, and a decrease of 12.7% in the amount of calculations, which can better meet the detection needs of the current industrial site for bearing appearance defects.

Key words: bearing defect detection, small target detection, YOLO11, lightweight, deep learning

CLC Number:  TP391.41
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