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Automatic detection of moisture damages in asphalt pavements from GPR data with deep CNN and IRS method
Automation in Construction ( IF 9.6 ) Pub Date : 2020-05-01 , DOI: 10.1016/j.autcon.2020.103119
Jun Zhang , Xing Yang , Weiguang Li , Shaobo Zhang , Yunyi Jia

Abstract Accurate detection and localization of moisture damage in asphalt pavements using Ground Penetrating Radars (GPR) has been attracting more and more interest in research. Existing approaches rely heavily on human efforts and expert experience and are thus both time and cost consuming and are also subject to accuracy issues caused by stochastic human errors. To address this issue, this paper presents an automated moisture damage detection and localization method by leveraging the state-of-the-art deep learning approach and newly proposed incremental random sampling (IRS) approach. First, 2.3 GHz Ground coupled GPR system was used to survey moisture damages on 16 asphalt pavement bridges to create three moisture damage datasets with different resolutions including 2135 moisture damages and 474 steel joints. On this basis, we propose mixed deep convolutional neural networks (CNN) including ResNet50 network, for feature extraction, and YOLO v2 network, for recognition, to detect and localize moisture damages. In addition, to prepare the input for the deep learning models, an IRS algorithm is proposed to generate suitable GPR images from GPR data to feed the CNN. Comprehensive experimental testing, analysis, and comparison of the proposed approaches are conducted. Experimental results demonstrated the promising performance and superiority of the proposed approaches in detecting and localizing moisture damages in asphalt pavements.

中文翻译:

基于深度CNN和IRS方法的GPR数据自动检测沥青路面湿损

摘要 使用探地雷达(GPR)准确检测和定位沥青路面湿损已引起越来越多的研究兴趣。现有方法在很大程度上依赖于人力和专家经验,因此既费时又费成本,并且还受到随机人为错误引起的准确性问题的影响。为了解决这个问题,本文提出了一种利用最先进的深度学习方法和新提出的增量随机采样 (IRS) 方法的自动湿气损伤检测和定位方法。首先,使用2.3 GHz地面耦合GPR系统对16座沥青路面桥梁的湿损进行调查,以创建三个不同分辨率的湿损数据集,包括2135个湿损和474个钢接头。以这个为基础,我们提出了混合深度卷积神经网络 (CNN),包括用于特征提取的 ResNet50 网络和用于识别的 YOLO v2 网络,以检测和定位水分损害。此外,为了为深度学习模型准备输入,提出了一种 IRS 算法,从 GPR 数据生成合适的 GPR 图像以馈送 CNN。对所提出的方法进行了全面的实验测试、分析和比较。实验结果表明,所提出的方法在检测和定位沥青路面湿损方面具有良好的性能和优越性。提出了一种 IRS 算法,从 GPR 数据生成合适的 GPR 图像以馈送 CNN。对所提出的方法进行了全面的实验测试、分析和比较。实验结果表明,所提出的方法在检测和定位沥青路面湿损方面具有良好的性能和优越性。提出了一种 IRS 算法,从 GPR 数据生成合适的 GPR 图像以馈送 CNN。对所提出的方法进行了全面的实验测试、分析和比较。实验结果表明,所提出的方法在检测和定位沥青路面湿损方面具有良好的性能和优越性。
更新日期:2020-05-01
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