Road Radar- An AI-Based Real-Time Road Condition Assessment System

Abstract

The decay of road infrastructure, particularly potholes and cracks, requires an efficient road condition assessment system to ensure timely maintenance, minimize risks, and reduce accidents. Traditional manual inspections are labor-intensive, time-consuming, and prone to inaccuracies, often resulting in delayed repairs and increased hazards. "RoadRadar – An AI-Based Real-Time Road Condition Assessment System" addresses these challenges using advanced computer vision segmentation models, such as YOLO (You Only Look Once) by Ultralytics, for real-time detection and segmentation of potholes and cracks. RoadRadar operates through two distinct yet interconnected modules. The first is a realtime module powered by Raspberry Pi 4 combined with a Google Coral TPU, designed for continuous on-the-go monitoring and detection of road anomalies, this uses a quantized YOLOv8n segmentation model. The second is a web-based module that allows users to upload videos or images for segmentation and detection of cracks and potholes, this contains multiple YOLO segmentation models ranging from a nano to an extra large YOLOv8 segmentation model. A diverse dataset of over 3000 road images, collected from Pakistani roads under varying weather conditions and environments, has been used to train these models, ensuring their effectiveness in real-world scenarios. This dual-module system increases RoadRadar’s versatility, offering both mobile and stationary applications for comprehensive road monitoring. Facilitating timely and accurate upkeep interventions, it contributes to road safety and infrastructure management.