RoadCare AI: Smart Road Health Monitoring and Maintenance Framework Using Deep Learning and YOLOv12
Keywords:
Automated alert system, Computer vision, Crack detection, Deep learning, Pothole detection, Real-time monitoring, Road damage detection, ResNet, Smart transportation, YOLOv12Abstract
Road infrastructure degradation has emerged as a critical challenge affecting transportation safety and economic efficiency, where the increasing presence of cracks and potholes contributes to accidents, vehicle damage, and delayed maintenance response. Within the paradigm of intelligent transportation systems, the RoadCare AI system is conceptualized as a deep learning-driven framework that enables automated detection of road damage through real-time image and video analysis. Traditional inspection methods rely on manual observation, which introduces delays, inconsistencies, and increased operational costs, thereby limiting effective infrastructure management. The proposed system integrates computer vision-based preprocessing with advanced deep learning models to enhance detection accuracy under diverse environmental conditions. The framework utilizes techniques such as noise reduction, contrast improvement, and normalization to ensure consistent input quality while enabling efficient feature learning. The enhanced data is processed through ResNet for feature extraction, which captures complex spatial patterns associated with road damage. Furthermore, the system employs YOLOv12 for high-speed object detection, enabling precise localization of cracks, potholes, and manholes in real-time scenarios. The integration of detection and alert mechanisms facilitates immediate reporting of road hazards, thereby supporting timely maintenance actions. Experimental evaluation demonstrates that the proposed YOLOv12 C3ECA DSA model achieves approximately 97% mAP with a training loss of 0.06 and validation loss of 0.05, confirming high accuracy and robust performance while maintaining computational efficiency. The system establishes an effective solution for automated road damage detection, contributing to improved road safety and intelligent urban management.
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