Machine Learning Approaches for Rapid Pothole Detection from 2D Images

TLDR: In this paper, the authors explored the viability of Deep Learning method for identifying the potholes from the processing of input video streams, and have also analyzed the Convolutional Neural Networks approach of deep learning through a self-built CNN model.

Abstract: Roads are inevitable parts of human civilisation, and construction of roads are considered under a Civil Engineering problem; but periodically these roads require maintenance and assessment, which is highly dependent on adequate and timely pavement condition data. Howbeit, in some cases, it has been found that the manual practice of collecting and analysing such data often leads to delay in reporting about the issues and fixing them on time. Also, repairing potholes is time consuming, and locating these manually is a huge task. We want to find out some mechanism which can identify the construction conditions as well as any kind of deformities on the road from the dashboard camera fitted into a car, and at the same time, can analyse the conditions of road surface and formation of potholes on the road. Optimization of manual pothole detection through automation has been a part of scientific research since long. Pothole identification has significantly been adapted in different screening and maintenance systems. But in our country, owing to the large number of road networks and wide variations in the nature of rural and urban road conditions, it is very difficult to identify potholes through an automated system. In this paper, we have looked into several methods of Computer Vision, like image processing techniques and object detection method so as to identify potholes from the video input stream to the system. But these techniques have been found to have different challenges like lighting conditions, interference in the line of vision on waterlogged roads, and inefficiency at night vision. Hence, furthermore, we have explored the viability of Deep Learning method for identifying the potholes from the processing of input video streams, and have also analysed the Convolutional Neural Networks approach of Deep Learning through a self-built CNN model. In this paper, the expediency of all the methods as well as their drawbacks have been discussed.