Comparative Analysis of Machine Learning Models for Smart Pothole Detection: Enhancing Road Safety and Maintenance Efficiency

In this paper, we propose a Smart Pothole Detection System designed to enhance road safety and improve maintenance efficiency through machine learning and geospatial analysis. Using smartphone applications, road images with corresponding XYZ coordinates are collected and labeled as either "pothole" or "no pothole". This dataset is utilized to train and evaluate multiple machine learning models, including Support Vector Machine (SVM), Logistic Regression, and Logistic Regression integrated with the AdaBoost ensemble method. These models are compared based on their accuracy, precision, recall, and F1-score, to determine the most effective solution for pothole detection. SVM emerged as the best-performing model, demonstrating high-dimensional data handling capabilities, while the AdaBoost-enhanced Logistic Regression model also performed competitively. Our system holds promise in contributing to real-time, automated pothole detection, which is essential for proactive road safety management and efficient maintenance planning.