Abstract: This paper introduces a deep learning–powered system designed to detect potholes from road images, assess their severity, and map their location. At the core of the system is a Convolutional Neural Network (CNN) trained on a curated dataset of road images containing both potholes and non-potholes. The model is able to accurately identify potholes and further categorize their severity into three levels—Minor, Moderate, or Major—based on its confidence score. To demonstrate the system’s functionality, detected potholes are assigned random locations within Bengaluru, and a detailed PDF report is generated. The report includes the detection results, supporting images, a severity distribution chart, and location information. The entire solution is deployed as a Flask-based web application, offering a simple interface where users can upload road images, receive real-time predictions, and download comprehensive reports.

Keywords: Pothole Detection, Deep Learning, Convolutional Neural Networks (CNN), Image Classification, Road Safety, Web Application (Flask).

Downloads: | DOI: 10.17148/IARJSET.2025.12829