An Automated Cyber-Physical Smart City Hazard Engine Integrating Deep Learning Computer Vision, Vectorized Non-Maximum Suppression, and Web-GIS Telemetry for Real-Time Road Infrastructure Anomaly Detection and Prioritized Incident Remediation
Keywords:
Automated road anomaly detection, Dynamic hazard prioritization, Smart city infrastructure, Urban transportation safety, Vectorized non-maximum suppression, Web-GIS spatial telemetry, YOLOv8 deep learningAbstract
Rapid metropolitan expansion and severe monsoonal weathering inflict acute structural degradation upon subsurface municipal utilities, routinely causing displaced manhole covers, fractured gratings, and exposed stormwater drains. Conventional civic maintenance relies on retrospective complaint queues or manual patrols, creating prolonged intervention delays that precipitate fatal traffic collisions and pedestrian injuries. This study presents PRJ_96 Intel, an automated, cyber-physical hazard surveillance and incident remediation engine developed for smart cities. The platform couples an asynchronous FastAPI backend with an edge-optimized YOLOv8 Nano convolutional neural network, an interactive Leaflet.js Web-GIS spatial telemetry dashboard, and an algorithmic repair prioritization engine. Trained on 4,850 annotated urban streetscape images across metropolitan Bengaluru, India, the lightweight vision detector achieves an overall mean Average Precision (mAP@0.5) of 90.1% while maintaining an inference throughput of 94.3 frames per second. To overcome proposal clustering under irregular pavement textures, a custom NumPy-vectorized Non-Maximum Suppression algorithm is formulated at an Intersection-over-Union threshold of 0.3. Detected hazards are geo-tagged and evaluated through a dynamic Priority Index (0–100) that balances neural detection certainty against localized pedestrian foot-traffic density ratings. Empirical stress testing demonstrates that the platform sustains an end-to-end response latency of 184 milliseconds under concurrent loads, successfully compressing emergency dispatch turnaround from several weeks to under four hours in high-risk zones while preserving privacy through automated visual redaction.
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