用物联网与视觉技术实时监控城市垃圾偷倒点，精准识别垃圾堆积。
Garbage Vulnerable Point Monitoring using IoT and Computer Vision
- 结合街边摄像头与目标检测算法，自动识别非法倾倒垃圾。
- YOLO11m模型在自建数据集上达到92.39%准确率，mAP@50为0.91。
- 可分析垃圾倾倒的小时、日、周规律，适合城市环卫管理使用。
本文提出一种利用物联网（IoT）与计算机视觉（CV）技术智能管理城市固体垃圾的方法，用于监测城市中垃圾易发点（GVPs）的非法倾倒行为。系统通过街边摄像头与目标检测算法，快速识别并持续监控垃圾倾倒事件。数据采集自印度特伦甘纳邦桑加雷迪区。在自建数据集上，对YOLOv8、YOLOv10、YOLO11m和RT-DETR进行了全面实验评估。其中，YOLO11m在垃圾检测任务中达到最高准确率92.39%，mAP@50为0.91，展现出优异性能。结果表明，该模型能有效识别垃圾倾倒事件。此外，系统还能捕捉垃圾投放的小时级、日级和周级趋势，实现全天候（含夜间）监控。
原文摘要 · Abstract (English)
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.
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