Pothole Detection Using Machine Learning Models

Abstract

Potholes are damage caused to the ground by the formation of water and wear and tear over time. According to statistical data, bad road conditions account for about one- third of the total road accidents which has been increasing exponentially. Potholes have become so common that it has become second nature for people to learn how to spot and avoid them, which causes further accidents. The need of the hour is to build a dependable pothole detection system to accurately detect potholes and warn the drivers and government officials in advance. The process to build such a system is divided into two steps i.e. collection of data and pothole identification. The first step is achieved by taking the data from already available data sets on the Internet. The other step includes labeling the potholes in the data set which is usually done manually. This paper focuses mainly on Visual-based techniques to identify the best detection method by comparing popular Machine Learning models and algorithms. The obtained data set is trained using various transfer learning techniques like You Only Look Once (YOLO)[1] and Single Shot Detector (SSD) [1]. Apart from transfer learning, this paper also focuses on some proposed techniques using Convolutional Neural Net- works (CNN) and classification algorithms like Support Vector Machine (SVM)[21] to identify and localize potholes. The actual size of potholes is calculated using morphological operations, which is a just a straightforward technique to analyze figures using set theory. To analyze every model and find the best model, each model is trained on different sizes of data sets and the obtained result is validated and examined by considering different aspects like speed and accuracy in mind.