Tunnel Lining Multi-Defect Detection Based on an Improved You Only Look Once Version 7 Algorithm

Abstract

In the domain of tunnel lining defect detection, object detection algorithms have been widely employed. However, existing algorithms suffer from inadequate extraction of global information and low detection accuracy. To address these issues, a novel algorithm called Tunnel Defect Detection You Only Look Once (TDD-YOLO) is proposed, leveraging the YOLOv7 framework. The TDD-YOLO algorithm incorporates several enhancements to improve global and local information extraction capabilities, thereby enhancing defect detection accuracy. Firstly, MobileViT is utilized as the backbone feature extraction network, augmenting the network’s ability to extract comprehensive information from both global and local contexts. Secondly, a Coordinate Attention (CA) module is introduced after the upsampling and downsampling stages of the feature pyramid network. This module highlights defect-related features while eliminating background interference. Lastly, a convolutional module called TP Block is devised to further enhance the network’s feature extraction capability with reduced computational complexity. To validate the effectiveness of the proposed algorithm, a comparative analysis is conducted against five existing algorithms: SSD, Faster-RCNN, EfficientDet, YOLOv5, and YOLOv7. Experimental results demonstrate that the TDD-YOLO algorithm achieves superior performance with an F1 score of 77.43% and a mean Average Precision (mAP) of 77.52%. These results surpass those of the other five algorithms, establishing the TDD-YOLO algorithm as the most accurate and suitable solution for defect detection tasks in tunnels.

Keywords

• Tunnel defects
• deep learning
• defect recognition
• object detection
• neural network