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| The lightweight object detection algorithm for obstacles in tunnel construction environments |
| WU Jiangtao1, LI Yingjie2
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1. Sinohydro Enginnering Bureau 4 Co., Ltd., Xining 810007, Qinghai, China;
2. School of Qilu Transportation, Shandong University, Jinan 250002, Shandong, China |
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Abstract Due to the frequent accidents at the tunnel construction site, using robots instead of manual safety inspections can effectively protect the 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 was 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 approximately 57% compared to the original YOLOv5-n algorithm, which reduced the hardware requirements of the model.
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Published: 28 March 2025
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