Real-time tunnel lining crack detection based on an improved You Only Look Once version X algorithm

ABSTRACT

To solve slow speed and low accuracy of traditional detection methods of tunnel lining cracks, especially under the complicated situation of tunnel in operation, this work proposed an improved You Only Look Once version X (YOLOX) tunnel lining crack image detection algorithm. First, Mobilenetv3 was used to replace YOLOX’s CSPDarknet network. The Efficient Channel Attention (ECA) module was then added to the enhanced feature extraction network, and the IOU loss function was replaced by the generalised IOU (GIOU) loss function. A tunnel crack image data set was constructed and used to compare the performance of the improved YOLOX algorithm with that of five other algorithms. The improved YOLOX algorithm solves the shortcomings of the other five algorithms. The results showed that the improved YOLOX algorithm had 82.48% F1 score and 87.28% AP value, which is higher than that of the other five algorithms at varying degrees. In addition, the data size of the improved YOLOX model was 51.2 M, which is 75.27% compressed compared to the YOLOX model. The time was 16.52 ms, and the FPS was 60.52 frames/s. Therefore, the proposed improved YOLOX algorithm can realise the high-speed, high-precision, real-time dynamic detection of tunnel lining cracks in complicated environments.

KEYWORDS:

• Tunnel
• deep learning
• crack detection
• channel attention mechanism

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This work was supported by National Natural Science Foundation of China: [grant no 50908234,52208421]; Research Innovation Project for Postgraduate of Central South University: [grant no 506021762]; Open Fund of National Engineering Research Center of Highway Maintenance Technology (Changsha University of Science & Technology): [grant no kfj220101].