Abstract: Due to the frequent accidents at the tunnel construction site, the use of robots instead of manual safety inspections can effectively protect the life safety of staff. However, most of the robots in this field currently require manual control and lack autonomous obstacle avoidance capabilities. Aiming at the obstacle detection method in tunnel construction environment, a lightweight model using improved YOLOv5 for obstacle detection was proposed. Firstly, the obstacle dataset in the tunnel scenario was constructed. Secondly, the backbone network was modified to a lightweight Shufflenet v2 network, and the activation function is modified to the SiLU function to improve the detection speed and reduce the amount of computation. Next, the coordinate attention mechanism was incorporated to improve the network's ability to learn and represent features. Finally, the neck convolution block was modified to GSConv, which reduced the calculation amount of the model and improved the detection accuracy of the algorithm. Comparative experiments on the dataset constructed in this research showed that the detection speed of the proposed method was increased by about 57% compared with the original YOLOv5-n algorithm, which greatly reduces the hardware requirements of the model.
吴江涛, 李英杰. 隧道施工环境中障碍物的轻量化目标检测算法[J]. 隧道与地下工程灾害防治, .
WU Jiangtao, LI Yingjie. The lightweight object detection algorithm for obstacles in tunnel construction environments#br#. Hazard Control in Tunnelling and Underground Engineering, 0, (): 1-.
