Citation

Cao Y, Liu Y, Sun Y, Su S, Wang F. Accid. Anal. Prev. 2024; 203: e107617.

Copyright

(Copyright © 2024, Elsevier Publishing)

DOI

10.1016/j.aap.2024.107617

PMID

38772193

Abstract

The rapid detection of internal rail defects is critical to maintaining railway safety, but this task faces a significant challenge due to the limited computational resources of onboard detection systems. This paper presents YOLOv8n-LiteCBAM, an advanced network designed to enhance the efficiency of rail defect detection. The network designs a lightweight DepthStackNet backbone to replace the existing CSPDarkNet backbone. Further optimization is achieved through model pruning techniques and the incorporation of a novel Bidirectional Convolutional Block Attention Module (BiCBAM). Additionally, inference acceleration is realized via ONNX Runtime. Experimental results on the rail defect dataset demonstrate that our model achieves 92.9% mAP with inference speeds of 136.79 FPS on the GPU and 38.36 FPS on the CPU. The model's inference speed outperforms that of other lightweight models and ensures that it meets the real-time detection requirements of Rail Flaw Detection (RFD) vehicles traveling at 80 km/h. Consequently, the YOLOv8n-LiteCBAM network is with some potential for industrial application in the expedited detection of internal rail defects.

Language: en

Keywords

Attention mechanisms; Internal rail defect detection; Lightweight networks; Model pruning; Real-time inference