Real-time Intelligent Drone Monitoring for Waste and Infrastructure Detection with Machine Learning Analysis

Roads in disrepair and insufficient trash disposal pose serious problems for urban areas, having an adverse effect on public health and the environment. To solve these problems, creative fixes are required. This study suggests a drone-based waste and pothole detection system that uses machine learning (ML)-driven reporting to enable prompt maintenance and interventions. This study describes the architecture and implementation of a system that uses IoT components, aerial photography, and the YOLO object identification model to collect and analyze visual data in real-time, precisely recognizing potholes and trash sites. Geo-tagging allows for the classification and mapping of the places indicated in captured photographs by a reporting system. Our method can improve urban maintenance by encouraging public safety and environmental cleanliness, giving priority to places that require immediate attention. The study shows how combining drone technology with machine learning (ML) might lead to sustainable solutions for managing urban infrastructure, greatly enhancing the quality of life in metropolitan areas.