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Deep Learning to Detect Road Distress from Unmanned Aerial System Imagery
Transportation Research Record: Journal of the Transportation Research Board ( IF 1.7 ) Pub Date : 2021-04-22 , DOI: 10.1177/03611981211004973
Long Ngo Hoang Truong 1 , Omar E. Mora 2 , Wen Cheng 2 , Hairui Tang 2 , Mankirat Singh 2
Affiliation  

Surface distress is an indication of poor or unfavorable pavement performance or signs of impending failure that can be classified into a fracture, distortion, or disintegration. To mitigate the risk of failing roadways, effective methods to detect road distress are needed. Recent studies associated with the detection of road distress using object detection algorithms are encouraging. Although current methodologies are favorable, some of them seem to be inefficient, time-consuming, and costly. For these reasons, the present study presents a methodology based on the mask regions with convolutional neural network model, which is coupled with the new object detection framework Detectron2 to train the model that utilizes roadway imagery acquired from an unmanned aerial system (UAS). For a comprehensive understanding of the performance of the proposed model, different settings are tested in the study. First, the deep learning models are trained based on both high- and low-resolution datasets. Second, three different backbone models are explored. Finally, a set of threshold values are tested. The corresponding experimental results suggest that the proposed methodology and UAS imagery can be used as efficient tools to detect road distress with an average precision score up to 95%.



中文翻译:

深度学习从无人机系统影像中检测道路困扰

表面不良是路面性能差或不利的指示,或即将发生故障的迹象,可以将其分类为断裂,变形或崩解。为了减轻道路故障的风险,需要有效的方法来检测道路窘迫。与使用对象检测算法检测道路遇险相关的最新研究令人鼓舞。尽管当前的方法是有利的,但其中一些方法似乎效率低下,耗时且成本高昂。由于这些原因,本研究提出了一种基于具有卷积神经网络模型的遮罩区域的方法,该方法与新的物体检测框架Detectron2一起训练利用从无人机系统(UAS)获取的道路图像的模型。为了全面了解建议模型的性能,在研究中测试了不同的设置。首先,深度学习模型是基于高分辨率和低分辨率数据集进行训练的。其次,探索了三种不同的骨干模型。最后,测试一组阈值。相应的实验结果表明,所提出的方法和UAS图像可以用作检测道路困境的有效工具,平均精度得分高达95%。

更新日期:2021-04-22
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