Automatic Humps and Pothole Detection

Abstract

This abstract introduces a novel system for the automatic detection of speed humps and potholes on roadways, aiming to enhance road safety and improve the driving experience. Speed humps and potholes are common road hazards that can lead to accidents, vehicle damage, and discomfort for passengers. Traditional methods of detecting these road anomalies rely heavily on manual inspections, which are time-consuming, costly, and often result in delayed maintenance. To address these challenges, our proposed system leverages cutting-edge computer vision and sensor technologies. The core components of our system include cameras, LiDAR (Light Detection and Ranging) sensors, and machine learning algorithms. Cameras capture real-time images of the road surface, while LiDAR sensors provide detailed depth information. The collected data is then processed through a deep learning model specifically trained for speed hump and pothole detection. The model identifies and classifies road anomalies, distinguishing between speed humps, potholes, and regular road surfaces.