Garbage Vulnerable Point Monitoring using IoT and Computer Vision

TL;DR

This paper presents an IoT and computer vision-based system for monitoring illegal waste dumping at vulnerable points in urban areas, demonstrating high accuracy in waste detection and pattern analysis.

Contribution

It introduces a novel integrated system combining IoT and advanced object detection models for real-time waste monitoring and pattern analysis at GVPs.

Findings

YOLO11m achieved 92.39% accuracy in waste detection

The system captures hourly, daily, and weekly dumping patterns

High precision with mAP@50 of 0.91

Abstract

This paper proposes a smart way to manage municipal solid waste by using the Internet of Things (IoT) and computer vision (CV) to monitor illegal waste dumping at garbage vulnerable points (GVPs) in urban areas. The system can quickly detect and monitor dumped waste using a street-level camera and object detection algorithm. Data was collected from the Sangareddy district in Telangana, India. A series of comprehensive experiments was carried out using the proposed dataset to assess the accuracy and overall performance of various object detection models. Specifically, we performed an in-depth evaluation of YOLOv8, YOLOv10, YOLO11m, and RT-DETR on our dataset. Among these models, YOLO11m achieved the highest accuracy of 92.39\% in waste detection, demonstrating its effectiveness in detecting waste. Additionally, it attains an mAP@50 of 0.91, highlighting its high precision. These findings confirm that the object detection model is well-suited for monitoring and tracking waste dumping events at GVP locations. Furthermore, the system effectively captures waste disposal patterns, including hourly, daily, and weekly dumping trends, ensuring comprehensive daily and nightly monitoring.