Intelligent Deep Learning based Pothole Detection and Reporting System

R. R, Sangoju Shreya, A. R
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引用次数: 2

Abstract

Road maintenance is crucial to prevent accidents due to potholes. Manual assessment of the road condition is a difficult task as it is a tedious process and requires lot of manpower. Thus, there is an increasing requirement for an automatic pothole identification system. In this paper, a solution is proposed to detect the potholes on roads automatically using the deep learning algorithms. Three deep learning namely, Convolutional Neural Network (CNN), Mask Region-based Convolutional Neural Network (Mask RCNN) and You Only Look Once (YOLOv3) are trained and tested with a dataset. The results of the three models are compared using evaluation metrics. This system also assembles hardware components for reporting the potholes in order to take actions for their repair and maintenance and warns the presence of potholes to drivers.
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基于智能深度学习的凹坑检测与报告系统
道路养护对于防止坑洼造成的事故至关重要。人工评估路况是一项艰巨的任务,因为它是一个繁琐的过程,需要大量的人力。因此,人们对自动坑位识别系统的需求越来越大。本文提出了一种利用深度学习算法自动检测道路坑洼的解决方案。三种深度学习即卷积神经网络(CNN)、基于掩码区域的卷积神经网络(Mask RCNN)和You Only Look Once (YOLOv3)用数据集进行训练和测试。使用评价指标对三种模型的结果进行比较。该系统还装配了一些硬件组件,用于报告坑洼,以便采取措施进行维修和保养,并向驾驶员发出坑洼存在的警告。
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