International Journal of Advanced Research in Computer and Communication Engineering

Abstract: Potholes continue to pose serious and recurring challenges to transportation safety, vehicle durability, and overall roadway efficiency. Their presence results in increased accident risk, higher fuel consumption, and substantial damage to vehicle suspension systems. Traditional detection approaches-such as manual inspection, complaint-based reporting, and periodic municipal surveys-fail to provide real-time, scalable, and accurate results, especially in dynamic traffic environments.

This research proposes an advanced, AI-driven pothole detection framework that integrates YOLO-based deep learning with multi-sensor fusion to significantly enhance detection reliability. The system utilizes RGB cameras for visual analysis, radar and LIDAR for 3D surface profiling, IMU sensors for vibration-based anomaly confirmation, ultrasonic sensors for depth estimation, and GPS modules for precise geo-tagging. A dedicated sensor-fusion layer ensures robust performance by validating detections across diverse environmental conditions including low-light scenarios, rain, uneven illumination, and partial occlusions. Furthermore, the system incorporates V2V communication to broadcast real-time alerts to nearby vehicles and uploads validated detections to a cloud-based analytical dashboard for predictive maintenance and road-health monitoring.

Experimental evaluation across varied terrains demonstrated a detection accuracy above 93%, with significantly reduced false positives compared to camera-only models. The results confirm that the proposed multi-sensor, deep-learning-driven architecture is highly suitable for integration into intelligent transportation systems, enabling safer mobility and smarter roadway infrastructure management.

Keywords: Pothole Detection, Deep Learning, Multi-Sensor Fusion, YOLO Algorithm, LIDAR, Radar Profiling, IMU Sensors, Ultrasonic Depth Measurement, GPS Geotagging, V2V Communication, Intelligent Transportation Systems (ITS), Cloud Analytics, Smart Road Maintenance

Downloads: | DOI: 10.17148/IJARCCE.2025.1411136