Detection of concealed cracks from ground penetrating radar images based on deep learning algorithm
Detection of concealed cracks from ground penetrating radar images based on deep learning algorithm
复制标题
基于深度学习算法的探地雷达图像隐蔽裂缝检测
DOI：
10.1016/j.conbuildmat.2020.121949
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发表时间：
2021-03-01
影响因子：
7.4
通讯作者：
Dong, Qiao
中科院分区：
文献类型：
--
作者：
Li, Shuwei;Gu, Xingyu;Dong, Qiao
沥青路面隐蔽裂缝的检测一直是一项具有挑战性的任务，由于这些裂缝的位置的不可见性。该研究提出了一种基于三维探地雷达（GPR）和深度学习模型自动识别和定位隐藏裂缝的有效方法。利用三维探地雷达和滤波处理，构建了一个数据集，包括303个探地雷达图像和1306个裂缝。接下来，You Only Look Once（YOLO）模型首次被引入作为深度学习模型，用于使用GPR数据检测隐藏的裂缝。结果表明，该方法对隐蔽裂缝的检测是可行的。与YOLO第3版相比，YOLO第4版（YOLOv4）和YOLO第5版（YOLOv5）即使在小数据集上也取得了明显的进步。YOLOv4模型的最快检测速度达到每秒10.16帧，仅使用中等CPU，YOLOv5模型的最佳mAP高达94.39%。此外，YOLOv4模型比YOLOv5模型具有更好的鲁棒性，能够准确区分隐蔽裂缝和伪裂缝。(C)2020爱思唯尔有限公司保留所有权利。
Detecting concealed cracks in asphalt pavement has been a challenging task due to the nonvisibility of the location of these cracks. This study proposes an effective method to automatically perform the recognition and location of concealed cracks based on 3-D ground penetrating radar (GPR) and deep learning models. Using a 3-D GPR and a filtering process, a dataset was constructed, including 303 GPR images and 1306 cracks. Next, You Only Look Once (YOLO) models were first introduced as deep learning models for detecting concealed cracks using GPR data. The results reveal that this proposed method is feasible for the detection of concealed cracks. Compared with YOLO version 3, YOLO version 4 (YOLOv4) and YOLO version 5 (YOLOv5) both achieve obvious progress even in a small dataset. The fastest detection speed of YOLOv4 models reaches 10.16 frames per second using only a medium CPU and the best mAP of YOLOv5 models is up to 94.39%. In addition, the YOLOv4 models show better robustness than the YOLOv5 models and could accurately distinguish between concealed cracks and pseudo cracks. (C) 2020 Elsevier Ltd. All rights reserved.