Journal of Shandong University(Engineering Science) ›› 2026, Vol. 56 ›› Issue (5): 1-12.doi: 10.6040/j.issn.1672-3961.0.2025.090

• Machine Learning & Data Mining •    

Research on the detection of train wheelset tread defects under complex conditions

Hou Tao, Ren Yikun*, Niu Hongxia   

  1. Hou Tao, Ren Yikun*, Niu Hongxia(School of Automation and Electrical Engineering, Lanzhou Jiaotong University, Lanzhou 730070, Gansu, China
  • Published:2026-10-19

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Abstract: To address the problems of poor detection performance for train wheelset tread defects under complex conditions and the mismatch among computational load, parameter count and accuracy, a detection method based on improved YOLOv9s was proposed. The sparse network structure Ghost-Inception was used to replace RepNCSPELAN4 in the backbone network of YOLOv9s, so as to reduce the model parameters and computational load, and improve detection accuracy and efficiency. The iEMA and SEAM attention mechanisms were introduced into the Neck network, and a new PagAM feature concatenation module was constructed to enhance the feature extraction capability under complex conditions. A RepNCSPELAN4-DCNv3 feature extraction and fusion module was built to strengthen the detection of defects at different scales. Experimental results showed that the parameters of the improved YOLOv9s network were reduced by 8.9%, the computational load was decreased by 11.4%, and the mean average precision was increased by 5.0%. The proposed method achieved improved detection accuracy on the premise of algorithm lightweight.

Key words: train wheelset tread, defect detection, YOLOv9s, lightweight, precision improvement

CLC Number: 

  • TP391
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