A Comprehensive Labeling Protocol and Real-time Inference Framework for Road Hazard Detection

Le résumé fourni par la source

Real-time detection of road surface damage and hazards represents a critical component in intelligent transportation systems and autonomous driving applications. However, existing methodologies often fail to capture the complexity and variability of real-world driving environments, while public datasets suffer from inconsistent labeling protocols and limited robustness to visual ambiguities. This paper introduces a comprehensive framework that addresses these challenges through: (1) a systematic annotation protocol optimized for real-world driving conditions, and (2) an edge-server hybrid inference architecture for efficient deployment. We collected 13,000 high-resolution road scene images and systematically re-annotated multiple public datasets to create a high-quality training corpus of 595,530 images. Visual artifacts such as shadows, surface contamination, and worn paint markings were systematically excluded through rigorous annotation guidelines. The proposed lightweight YOLO-based detector, deployed within a hybrid edge-server pipeline, achieves low-latency, high-throughput inference while maintaining high detection accuracy. Comprehensive evaluations on large-scale real-world benchmarks demonstrate that our approach reduces server load by 77%, achieves 23.0 FPS inference speed, and attains a competitive mAP of 0.7147, confirming its suitability for deployment in safety-critical in-vehicle systems.

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