Detecting Potholes from Dashboard Camera Images Using Ensemble of Classification Mechanisms
Detecting Potholes from Dashboard Camera Images Using Ensemble of Classification Mechanisms
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DOI：
10.1109/smartcomp58114.2023.00031
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发表时间：
2023-06
期刊：
影响因子：
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通讯作者：
Hiroo Bekku;Miku Minami;Takafumi Kawasaki;J. Nakazawa
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文献类型：
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作者：
Hiroo Bekku;Miku Minami;Takafumi Kawasaki;J. Nakazawa
由于老化，道路可能会出现坑洼等路面损坏，影响交通。由于道路调查成本高昂，定期检查道路损坏情况很困难。开发一种根据行车记录仪图像自动检测坑洼和其他道路损坏的系统可以实现廉价的道路检查，并可以全面改善长期忽视道路损坏的问题。去年，我们在东京江户川市进行了演示实验，使用现有的基于图像的道路损坏检测方法。从该实验中，我们发现实际道路上坑洼的检测经常会导致阴影和沙井检测出现误报。在本研究中，我们提出了一种减少坑洞检测误报的方法，该方法通过演示实验被认为是一个问题。由于基于仅坑洞数据集的评估不切实际，我们通过添加阴影和沙井图像构建了用于评估的数据集。我们的方法由两个主要部分组成：数据增强和对象检测模型的分类机制集合。对重建坑洞数据集的测试结果表明，与现有方法相比，评估目标检测性能的平均精度（AP）以及精度和召回率的调和平均值F1均得到了提高。我们的新方法预计将成为针对可能发生误报以及误报比漏报更被视为问题的任务和情况的有效管道，因为它们不依赖于坑洼的领域。
Road damage such as potholes may occur on roads due to aging, which may affect traffic. Periodic inspections of road damages are difficult due to the high cost of road surveys. The development of a system that automatically detects potholes and other road damages from dash cam images can allow inexpensive road inspections and can overall improve the problem of the long-term overlook of road damages. Last year, we conducted a demonstration experiment in Edogawa City, Tokyo, using an existing image-based road damage detection method. From that experiment, we found that the detection of potholes on actual roads often causes false positives in detecting shadows and manholes. In this study, we propose a method to reduce false positives in pothole detection, which was considered to be a problem through the demonstration experiment. Since the evaluation based on a pothole-only dataset is not practical, we constructed a dataset for evaluation by adding shadow and manhole images. Our method consists of two main components: data augmentation and an ensemble of classification mechanisms for object detection models. The result of the test on the reconstructed pothole dataset showed that the Average Precision (AP), which is a measure to evaluate the performance of object detection, and F1, which is the harmonic mean of precision and recall, were improved compared to the existing method. Our new method is expected to be an effective pipeline for tasks and situations where false positives are likely to occur and where false positives are more considered as an issue than false negatives, given that they are not dependent on the domain of potholes.