Scholarlink

<p>With the increasing tunnel construction projects in China, geological hazards and construction accidents in tunnels occur frequently. The widely applied intelligent detection of ground-penetrating radar (GPR) for tunnel advance geological forecast (TAGF) rarely involves research and application exploration on irregular structures. The GPR dataset for TAGF in the Guangxi region meets the requirements for isomorphic source domain feature extraction. This paper proposes a methodology for creating polygon labeling of irregular structures and develops an image segmentation approach based on the Yolov5s deep learning framework, establishing the polygon-Yolov5s intelligent forecasting network for irregular geological hazards in GPR tunnel detection. Through case trials using both numerical and actual TAGF datasets, the feasibility and effectiveness of the proposed algorithm are validated by using the SSD algorithm and the traditional Yolov5s algorithm. The effective utilization of intelligent interpretation systems for irregular geological hazards would improve the efficiency and accuracy of geological forecasting and operational maintenance detection.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Deep learning-based tunnel advance geological forecast for polygon targets via regional GPR data

  • 期刊论文

  • 发表日期：

    2025年7月31日

查看全文
  • Ting-wei Yang,
  • Jing-he Li,
  • Ye-hui Cao,
  • Bin Xiong,
  • Liu-ye Wei

摘要

With the increasing tunnel construction projects in China, geological hazards and construction accidents in tunnels occur frequently. The widely applied intelligent detection of ground-penetrating radar (GPR) for tunnel advance geological forecast (TAGF) rarely involves research and application exploration on irregular structures. The GPR dataset for TAGF in the Guangxi region meets the requirements for isomorphic source domain feature extraction. This paper proposes a methodology for creating polygon labeling of irregular structures and develops an image segmentation approach based on the Yolov5s deep learning framework, establishing the polygon-Yolov5s intelligent forecasting network for irregular geological hazards in GPR tunnel detection. Through case trials using both numerical and actual TAGF datasets, the feasibility and effectiveness of the proposed algorithm are validated by using the SSD algorithm and the traditional Yolov5s algorithm. The effective utilization of intelligent interpretation systems for irregular geological hazards would improve the efficiency and accuracy of geological forecasting and operational maintenance detection.