AI BASED POTHHOLE DETECTION

Abstract

Road safety is a major concern in developing and developed countries due to the increasing number of accidents caused by poor road conditions. Potholes are one of the primary causes of vehicle damage, traffic congestion, and accidents. Traditional methods of road inspection are manual, time-consuming, and inefficient. This paper presents an Artificial Intelligence (AI)-based pothole detection system that automates the identification and reporting of potholes using image processing and deep learning techniques.
The proposed system utilizes a convolutional neural network (CNN) model to detect potholes from images or video streams captured through cameras mounted on vehicles or smartphones. The system processes real-time data and identifies potholes with high accuracy. Once detected, the system can store the location using GPS and send alerts to relevant authorities for maintenance.
The model is trained using a dataset consisting of labeled images of roads with and without potholes. Various preprocessing techniques such as image resizing, normalization, and augmentation are applied to improve accuracy. The trained model is then deployed on embedded systems or mobile applications for real-time detection.