Intelligent recognition of ground penetrating radar images in urban road detection: a deep learning approach

IF 3.6 2区 工程技术 Q1 ENGINEERING, CIVIL Journal of Civil Structural Health Monitoring Pub Date : 2024-07-01 DOI:10.1007/s13349-024-00818-5
Fujun Niu, Yunhui Huang, Peifeng He, Wenji Su, Chenglong Jiao, Lu Ren
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Abstract

In recent years, urban road collapse incidents have been occurring with increasing frequency, particularly in populous cities. To mitigate road collapses, geophysical prospecting plays a crucial role in urban road inspections. Ground Penetrating Radar (GPR), a non-destructive technology, is extensively employed for detecting urban road damage, with manual interpretation of GPR images typically used to identify buried objects. Nonetheless, manual interpretation methods are not only inefficient but also subjective, as they rely on the interpreter's experience, thereby affecting the interpreting reliability. This study investigates the distribution and causes of road collapses and develops a deep learning-based intelligent recognition model using GPR detection images of urban roads in cities of the South China as original samples. The finding reveal that road collapses are concentrated in the months of July and August, mainly caused by pipe leakage and rainfall. Common anomalies in urban road GPR detection comprise seven types of target objects, including cavity, pipeline, etc., with standard GPR images acquired through outdoor field experiments. Utilizing GPR forward simulation and image augmentation methods to expand the sample size, as well as generating anchor box dimensions through clustering analysis, have all been proven to effectively improve the model's performance. The urban road GPR image intelligent recognition model, based on the YOLOv4 algorithm, achieves a detection accuracy of up to 85%, proving effective in GPR detection of urban roads in cities of North China. This research offers valuable insights for the future application of deep learning-based image recognition algorithms in urban road GPR detection.

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城市道路探测中地面穿透雷达图像的智能识别:一种深度学习方法
近年来,城市道路塌陷事件发生得越来越频繁,尤其是在人口众多的城市。为减少道路塌陷,地球物理勘探在城市道路检测中发挥着至关重要的作用。地面穿透雷达(GPR)是一种非破坏性技术,被广泛用于探测城市道路的损坏情况,通常使用人工判读 GPR 图像来识别被埋物体。然而,人工判读方法不仅效率低下,而且依赖于判读人员的经验,具有一定的主观性,从而影响判读的可靠性。本研究以华南地区城市道路的 GPR 检测图像为原始样本,研究了道路塌陷的分布和成因,并开发了基于深度学习的智能识别模型。研究结果表明,道路塌陷主要集中在七八月份,主要原因是管道渗漏和降雨。城市道路 GPR 检测中常见的异常情况包括空洞、管道等七种类型的目标物体,其标准 GPR 图像是通过室外现场实验获取的。实践证明,利用 GPR 正向模拟和图像增强方法扩大样本量,以及通过聚类分析生成锚箱尺寸,都能有效提高模型的性能。基于 YOLOv4 算法的城市道路 GPR 图像智能识别模型的检测准确率高达 85%,在华北地区城市道路 GPR 检测中证明是有效的。这项研究为未来基于深度学习的图像识别算法在城市道路 GPR 检测中的应用提供了有价值的启示。
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来源期刊
Journal of Civil Structural Health Monitoring
Journal of Civil Structural Health Monitoring Engineering-Safety, Risk, Reliability and Quality
CiteScore
8.10
自引率
11.40%
发文量
105
期刊介绍: The Journal of Civil Structural Health Monitoring (JCSHM) publishes articles to advance the understanding and the application of health monitoring methods for the condition assessment and management of civil infrastructure systems. JCSHM serves as a focal point for sharing knowledge and experience in technologies impacting the discipline of Civionics and Civil Structural Health Monitoring, especially in terms of load capacity ratings and service life estimation.
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