Lightweight GCD-YOLOv5 for Real-Time Obstacle Detection in Tunnel Pipe Jacking Operations

Abstract

The identification of tunnel pipe jacking obstacles is usually carried out in harsh environments, and timely and accurate identification can avoid unnecessary economic and labor losses. However, the currently commonly used obstacle recognition models are complex in structure and have long recognition feedback times. Therefore, this study proposes a tunnel pipe jacking obstacle recognition model based on an improved You Only Look Once version 5. The model is optimized through pruning and knowledge distillation techniques to enhance its lightweight characteristics and accuracy. The experiments were conducted using the COCO dataset and a custom dataset consisting of 2667 tunnel shield tunneling obstacle images. The optimized model achieved an 88.6% reduction in the number of parameters, an 84.2% reduction in floating-point operations, a 62.5% reduction in memory usage, and a 90.1% reduction in response time. In real-world testing, the model achieved an accuracy of 94.0% and a processing speed of 75 frames per second (FPS), outperforming traditional YOLOv5 and other lightweight YOLOv5 variants such as M-YOLOv5, S-YOLOv5, PL-YOLOv5, and C-YOLOv5. Using evaluation metrics such as mean Average Precision (mAP), the proposed model demonstrated high efficiency and effectiveness in real-time obstacle recognition for tunnel construction. The model provides a new technological approach for safety management in tunnel construction while improving computational efficiency and maintaining high recognition accuracy.