Smartphone-Based Pothole and Speed-Breaker Detection System

Potholes on roads are a widespread problem, causing damage to vehicles and endangering lives of drivers. Traditional methods for detecting potholes involve manual inspections, which are time-consuming and expensive. This research presents a novel approach to detect potholes on roads using accelerometer data. Accelerometer data can be collected using low-cost sensors and processed using machine learning algorithms to identify potholes automatically. The proposed approach involves collecting accelerometer data from a vehicle as it drives over a road surface. The data is processed using SVM to identify features that are indicative of potholes, such as sudden changes in acceleration or vibrations. The algorithm is trained and tested on secondary and primary datasets using labeled data to detect potholes accurately. Experimental results during training and testing on primary data show that the SVM is effective at detecting potholes with an accuracy of 93.3%. Pothole detection could be faster and cheaper with this method, leading to better roads and less risky driving conditions for drivers. This research demonstrates the feasibility of using accelerometer data to automate pothole detection, providing a promising avenue for future research in this area.