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Garbage Vulnerable Point Monitoring using IoT and Computer Vision

R. Kumar, A. Lall, S. Chaudhari, M. Kale, A. Vattem

TL;DR

This work integrates IoT-enabled street cameras with advanced computer vision to detect illegal waste dumping at garbage vulnerable points (GVPs) in urban settings. By collecting a two-month, street-level dataset from Sangareddy, the authors train and evaluate multiple object detectors (YOLOv8, YOLOv10, YOLO11m, RT-DETR), finding YOLO11m to achieve the highest accuracy ($92.39\%$) and $mAP@50$ of $0.91$, enabling reliable real-time monitoring. The methodology includes an end-to-end training pipeline with ROI-based processing, data augmentation, and a robust computational setup, alongside detailed temporal analyses (hourly, daily, weekly) and a hardware-cost assessment. The results demonstrate the system’s potential to inform waste-management scheduling and enforcement, paving the way for scalable, data-driven urban cleanliness and sustainability strategies, with future work targeting deployment across multiple GVPs.

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.

Garbage Vulnerable Point Monitoring using IoT and Computer Vision

TL;DR

This work integrates IoT-enabled street cameras with advanced computer vision to detect illegal waste dumping at garbage vulnerable points (GVPs) in urban settings. By collecting a two-month, street-level dataset from Sangareddy, the authors train and evaluate multiple object detectors (YOLOv8, YOLOv10, YOLO11m, RT-DETR), finding YOLO11m to achieve the highest accuracy () and of , enabling reliable real-time monitoring. The methodology includes an end-to-end training pipeline with ROI-based processing, data augmentation, and a robust computational setup, alongside detailed temporal analyses (hourly, daily, weekly) and a hardware-cost assessment. The results demonstrate the system’s potential to inform waste-management scheduling and enforcement, paving the way for scalable, data-driven urban cleanliness and sustainability strategies, with future work targeting deployment across multiple GVPs.

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.

Paper Structure

This paper contains 15 sections, 5 figures, 2 tables.

Table of Contents

  1. Introduction
  2. METHODOLOGY
  3. IoT-Based camera setup
  4. Data collection campaign
  5. CV based models
  6. End-to-End Pipeline for Training and Detection
  7. Evaluation metrics
  8. Computational setup
  9. Results and Discussion
  10. Comparison of object detection models
  11. Waste disposal behavior by hour
  12. Daily waste patterns
  13. Weekday analysis of waste dumping activities
  14. Hardware cost analysis
  15. Conclusion

Figures (5)

  • Figure 1: Automating the end-to-end process for training and object detection
  • Figure 2: The hourly waste coverage within the Region of Interest (RoI) at the GVP was computed as an average over the entire data collection timeframe.
  • Figure 3: Different activities affecting the waste distribution at the GVP.
  • Figure 4: Daily average percentage of ROI covered with waste at the GVP for the two months.
  • Figure 5: Weekly distribution of waste at GVP