Road Rutting Detection using Deep Learning on Images
Road Rutting Detection using Deep Learning on Images
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DOI：
10.1109/bigdata55660.2022.10020458
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发表时间：
2022-09
期刊：
影响因子：
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通讯作者：
Poonam Kumari Saha;Deeksha M. Arya;Ashutosh Kumar;Hiroya Maeda;Y. Sekimoto
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文献类型：
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作者：
Poonam Kumari Saha;Deeksha M. Arya;Ashutosh Kumar;Hiroya Maeda;Y. Sekimoto
道路车辙是一种严重的道路病害，可导致道路过早失效，从而导致早期和昂贵的维护成本。在过去的几年里，使用图像处理技术和深度学习进行道路损伤检测的研究正在积极进行。然而，这些研究大多集中在检测裂缝，坑洞，以及它们的变种。很少有研究已经做了检测道路车辙。本文提出了一种新的道路车辙数据集，包括949图像，并提供对象级和像素级的注释。在所提出的数据集上部署目标检测模型和语义分割模型来检测道路车辙，并对模型预测进行定量和定性分析，以评估模型性能并确定使用所提出的方法检测道路车辙所面临的挑战。目标检测模型YOLOXs实现了61.6%的mAP@IoU=0.5，语义分割模型PSPNet（Resnet-50）实现了54.69的IoU和72.67的准确率，从而为未来类似工作提供了基准准确率。建议的道路车辙数据集和我们的研究结果将有助于加速使用深度学习检测道路车辙的研究。
Road rutting is a severe road distress that can cause premature failure of the road incurring early and costly maintenance costs. Research on road damage detection using image processing techniques and deep learning are being actively conducted in the past few years. However, these researches are mostly focused on the detection of cracks, potholes, and their variants. Very few research has been done on the detection of road rutting. This paper proposes a novel road rutting dataset comprising 949 images and provides both object-level and pixel-level annotations. Object detection models and semantic segmentation models were deployed to detect road rutting on the proposed dataset, and quantitative and qualitative analysis of model predictions were done to evaluate model performance and identify challenges faced in the detection of road rutting using the proposed method. Object detection model YOLOXs achieves mAP@IoU=0.5 of 61.6% and semantic segmentation model PSPNet (Resnet-50) achieves IoU of 54.69 and accuracy of 72.67, thus providing a benchmark accuracy for similar work in future. The proposed road rutting dataset and the results of our research study will help accelerate the research on the detection of road rutting using deep learning.