ROAD GUARD AI: AN INTELLIGENT DEEP LEARNING AND COMPUTER VISION FRAMEWORK FOR AUTOMATE ROAD DAMAGE IDENTIFICATION AND ALERT GENERATION
DOI:
https://doi.org/10.5281/zenodo.21410061Keywords:
Road Damage Detection, Computer Vision, Deep Learning, YOLO v12, ResNet, Real Time Monitoring, Smart Transportation, Automated Alert SystemAbstract
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 Road Guard AI system is conceptualized as a deep learning driven framework that enables automated detection of road damages through real time image and video analysis. Traditional inspection methods rely on manual observation which introduces delays inconsistencies and increased operational costs there by 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 where within the contemporary framework of computer vision the framework is articulated as a robust construct dedicated to improving input quality through image enhancement operations. Anchored in the domain of computer vision based analytics 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 damages.
Furthermore the system employs YOLO v12 for high speed object detection enabling precise localization of cracks and potholes in real time scenarios where positioned within the evolving landscape of smart infrastructure systems the architecture is conceptualized as an intelligent detection ecosystem that integrates multi level feature learning and localization processes thereby realizing accurate and efficient outcomes. The integration of detection and alert mechanisms facilitates immediate reporting of road hazards thereby supporting timely maintenance actions.
The experimental evaluation demonstrates that the proposed approach achieves high accuracy and robust performance while maintaining computational efficiency where grounded in the domain of AI driven monitoring systems the framework is envisioned as a scalable paradigm that enables reliable detection and real time response ultimately delivering improved safety outcomes. The system therefore establishes an effective solution for automated road damage detection contributing to improved road safety and intelligent urban management.
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