Advancing Urban Infrastructure Safety: Modern Research in Deep Learning for Manhole Situation Supervision Through Drone Imaging and Geographic Information System Integration

Abstract

This paper research introduces a cutting-edge approach to enhancing urban infrastructure safety through the integration of modern technologies. Leveraging state of the art deep learning techniques, specifically the recent object detection models, with a focus on YOLOv8, we propose a system for supervising and detecting manhole situations using drone imagery and GPS location data. Our experiments with object detection models demonstrate exceptional results, showcasing high accuracy and efficiency in the detection of manhole covers and potential hazards in real-time drone imagery. The best trained model is YOLOv8, which achieves a mAP@50 rate of 89% and a Precision rate of 95%, surpassing existing methods. By combining this visual information with precise GPS location data, our system offers a comprehensive solution for monitoring urban landscapes. The integration of YOLOv8 not only improves the efficiency of manhole detection but also contributes to proactive maintenance and risk mitigation in urban environments. This research represents also a significant step forward in leveraging modern research methodologies, and the outstanding results of our trained models underscore the effectiveness of Object detection models in addressing critical infrastructure challenges.