Poor road conditions, such as potholes, are a nuisance to society, which would annoy passengers, damage vehicles, and even cause accidents. Thus, detecting potholes is an important step toward pavement maintenance and rehabilitation to improve road conditions. Potholes have different shapes, scales, shadows, and illumination effects, and highly complicated backgrounds can be involved. Therefore, detection of potholes in road images is still a challenging task. In this study, we focus on pothole detection in 2D vision and present a new method to detect potholes based on location-aware convolutional neural networks, which focuses on the discriminative regions in the road instead of the global context. It consists of two main subnetworks: the first localization subnetwork employs a high recall network model to find as many candidate regions as possible, and the second part-based subnetwork performs classification on the candidates on which the network is expected to focus. The experiments using the public pothole dataset show that the proposed method could achieve high precision (95.2%), recall (92.0%) simultaneously, and outperform the most existing methods. The results also demonstrate that accurate part localization considerably increases classification performance while maintains high computational efficiency. The source code is available at https://github.com/hanshenchen/pothole-detection.

中文翻译：

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使用位置感知卷积神经网络进行坑洞检测

坑洼之类的恶劣道路状况对社会造成困扰，会惹恼乘客，损坏车辆，甚至引发事故。因此，检测坑洼是迈向路面维护和修复以改善道路状况的重要一步。坑洞具有不同的形状，比例，阴影和照明效果，并且可能涉及高度复杂的背景。因此，检测道路图像中的坑洼仍然是一项艰巨的任务。在这项研究中，我们专注于2D视觉中的坑洞检测，并提出了一种基于位置感知卷积神经网络的坑洞检测新方法，该方法专注于道路上的区分区域而不是全局上下文。它由两个主要子网组成：第一个本地化子网络采用高召回率网络模型来查找尽可能多的候选区域，第二个基于部分的子网络对期望网络聚焦的候选者进行分类。使用公共坑洞数据集进行的实验表明，该方法可以实现较高的精度（95.2％），同时召回率（92.0％），并且优于大多数现有方法。结果还表明，准确的零件定位可显着提高分类性能，同时保持较高的计算效率。可从https://github.com/hanshenchen/pothole-detection获取源代码。使用公共坑洞数据集进行的实验表明，该方法可以实现较高的精度（95.2％），同时召回率（92.0％），并且优于大多数现有方法。结果还表明，准确的零件定位可显着提高分类性能，同时保持较高的计算效率。可从https://github.com/hanshenchen/pothole-detection获取源代码。使用公共坑洞数据集进行的实验表明，该方法可以实现较高的精度（95.2％），同时召回率（92.0％），并且优于大多数现有方法。结果还表明，准确的零件定位可显着提高分类性能，同时保持较高的计算效率。可从https://github.com/hanshenchen/pothole-detection获取源代码。