University Science Park UNIZA, University of Žilina, Univerzitná 8215/1, 010 26 Žilina, Slovakia.
Sensors (Basel). 2022 Nov 17;22(22):8878. doi: 10.3390/s22228878.
Road discrepancies such as potholes and road cracks are often present in our day-to-day commuting and travel. The cost of damage repairs caused by potholes has always been a concern for owners of any type of vehicle. Thus, an early detection processes can contribute to the swift response of road maintenance services and the prevention of pothole related accidents. In this paper, automatic detection of potholes is performed using the computer vision model library, You Look Only Once version 3, also known as Yolo v3. Light and weather during driving naturally affect our ability to observe road damage. Such adverse conditions also negatively influence the performance of visual object detectors. The aim of this work was to examine the effect adverse conditions have on pothole detection. The basic design of this study is therefore composed of two main parts: (1) dataset creation and data processing, and (2) dataset experiments using Yolo v3. Additionally, Sparse R-CNN was incorporated into our experiments. For this purpose, a dataset consisting of subsets of images recorded under different light and weather was developed. To the best of our knowledge, there exists no detailed analysis of pothole detection performance under adverse conditions. Despite the existence of newer libraries, Yolo v3 is still a competitive architecture that provides good results with lower hardware requirements.
道路凹凸不平和道路裂缝等路况差异在我们日常通勤和旅行中经常出现。由坑洼造成的损坏维修成本一直是任何类型车辆所有者关注的问题。因此，早期的检测过程有助于道路养护服务的快速响应，防止与坑洼相关的事故。在本文中，使用计算机视觉模型库，即 You Look Only Once 版本 3（也称为 Yolo v3），自动检测坑洼。行驶过程中的光线和天气自然会影响我们观察道路损坏的能力。这些不利条件也会对视觉目标检测器的性能产生负面影响。这项工作的目的是研究不利条件对坑洼检测的影响。因此，本研究的基本设计由两个主要部分组成：（1）数据集创建和数据处理，以及（2）使用 Yolo v3 进行数据集实验。此外，我们还将 Sparse R-CNN 纳入了我们的实验中。为此，开发了一个由在不同光线和天气下记录的图像子集组成的数据集。据我们所知，目前还没有关于在不利条件下进行坑洼检测性能的详细分析。尽管存在更新的库，但 Yolo v3 仍然是一种具有竞争力的架构，它可以在较低的硬件要求下提供良好的结果。
