A Deep Learning Approach For Pothole Detection

Abstract

Road accident detection and avoidance are a more difficult and challenging problem in India as poor quality of construction materials get used in road drainage system construction. Due to the above problems, roads get damaged early and potholes appear on the roads which cause accidents. According to a report submitted by the Ministry of Road Transport and Highways transport research wing New Delhi in 2017, approximately 4,64,910 accidents happen per year in India. This paper proposed a deep learning-based model that can detect potholes early using images and videos which can reduce the chances of an accident. This model is basically based on Transfer Learning, Faster Region-based Convolutional Neural Network(F-RCNN) and Inception-V2. There are many models for pothole detection that uses the accelerometer (without using images and videos) with machine learning techniques, but a less number of pothole detection models can be found which uses only machine learning techniques to detect potholes. The results of this work have shown that our proposed model outperforms other existing techniques of potholes detection.