Design of a Deep Learning Based Pothole Detection System.

Abstract

With an increasing number of potholes on the roads that take a while before they are repaired, their adverse effects like accidents, traffic jam, flooding during the rainy season and damage to motor vehicles keep affecting road users. However automating the process of the location and detection of these potholes can cut down the amount of time it takes for these potholes to be repaired from when they are first located. This report therefore presents the Design of a Deep Learning Based Pothole Detection System which involves a deep analysis on the use of machine learning and Convolutional Neural Networks in computer vision and object detection. This study will involve training a machine learning model with pothole dataset and later developing a model that can detect the presence of these potholes on the roads. Data collection was done by taking pictures in form of videos of different roads inside a moving vehicle by use of a mobile phone placed on a car windscreen. A total of 836 images was collected, the images were resized and labelled by drawing bounding boxes around the potholes in the images. The images were then converted to a format compatible with the training algorithm, the COCO dataset format. For training the EfficientDet algorithm was used which is a high accuracy, high efficiency algorithm with each layer images go through different kinds of optimization till the box layer. Model training using the effcientdet algorithm was successful and a machine learning model that detects potholes with an Average Precision of 0.52515274 was developed. A pothole dataset of 836 images was collected and is stored in a database.