Development of an AI-Based System for Real-Time Pothole Detection, Severity Classification and Volume Estimation in Kenya

This research focuses on the development of an AI-based pothole detection system to improve road maintenance and safety in Kenya. Traditional pothole detection methods rely on manual inspection and outdated techniques, which are time-consuming, inefficient, and prone to errors. The developed AI-based system employs the YOLO (You Only Look Once) object detection framework to conduct real-time pothole detection. Data augmentation techniques such as rotation and flipping, brightness adjustment and blur effects were used on images during image preprocessing to enhance model reliability enabling it to perform well under different environmental conditions, lighting variations, and road textures. The AI system integrates the following key functionalities: Pothole detection, severity classification following the Kenyan Road Design Manual Volume 5, Part 1: Pavement Condition Survey, GPS tagging and volume estimation, and predicting the amount of materials needed for pothole repairs. The model was implemented by PyTorch on Ubuntu, and multiple performance metrics were used to evaluate the model's effectiveness, including: Mean Average Precision (mAP@50 & mAP@50-95), Intersection over Union (IoU), Precision, Recall, F1 Score and Inference Time. We built a web-based application that incorporates the trained AI model to detect and classify potholes in real time through a user-friendly system. The application allows users to both upload pictures and stream real-time video images for performing automated pothole identification.. The developed pothole detection system reduces manual work by delivering rapid and precise monitoring of road conditions. When implemented in Kenya, this system will improve road safety while lowering vehicle damage and increasing repair operation efficiency, thereby creating meaningful impacts on road management.