Underground infrastructure detection and localization using deep learning enabled radargram inversion and vision based mapping
Underground infrastructure detection and localization using deep learning enabled radargram inversion and vision based mapping
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DOI：
10.1016/j.autcon.2023.105004
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发表时间：
2023-10
影响因子：
10.3
通讯作者：
Mengjun Wang;Da Hu;Junjie Chen;Shuai Li
中科院分区：
文献类型：
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作者：
Mengjun Wang;Da Hu;Junjie Chen;Shuai Li
地下管道罢工，一个紧迫的问题，由于不准确的地下数据，本文解决的管道检测和定位框架。首先，通过模拟探地雷达（GPR）对城市道路的沿着扫描，并利用生成对抗网络（GAN）技术增强其真实感，生成丰富的雷达图，以缓解雷达图数据不足的问题。其次，深度学习网络被设计为直接从雷达图重建介电常数图，以进行准确的管道检测和表征，而不是检测雷达图中的管道特征。第三，采用同步定位和测绘（SLAM）进行GPR位置估计，从而实现管道的精确地理配准。该方法在介电常数图重建中获得了0.957的R平方（R2 15）值，在管道检测中获得了96.2%的精度。此外，它提供了令人满意的性能与偏差的1.71%的深度和直径的检测管道的20.44%。真实世界的实验验证了所提出的框架的有效性，突出了其防止挖掘事故，减少项目延误的潜力，并为公用事业公司，承包商和城市规划者提供了显着的好处
Underground pipeline strikes, a pressing problem due to inaccurate subsurface data, are addressed in this paper with a pipeline detection and localization framework. First, abundant radargrams are generated to relieve radargram data shortage by simulating Ground Penetrating Radar (GPR) scans along the urban roadway and enhancing their realism with Generative Adversarial Network (GAN) technique. Second, a deep learning network is designed to directly reconstruct permittivity maps from radargrams for accurate pipeline detection and characterization, instead of detecting pipeline features within the radargram. Third, Simultaneous Localization and Mapping (SLAM) is employed for GPR position estimation, enabling precise georegistration of pipelines. The proposed method attains an R-squared (R2 15) value of 0.957 in permittivity map reconstruction and 96.2% precision in pipeline detection. Additionally, it provides satisfactory performance with a deviation of 1.71% in depth and 20.44% in diameter for the detected pipelines. Real-world experiments validate the effectiveness of the proposed framework, highlighting its potential to prevent excavation accidents, reduce project delays, and offer significant benefits to utility companies, contractors, and urban planners