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.

翻译：本文提出一种利用物联网（IoT）与计算机视觉（CV）技术监测城市区域垃圾易发点（GVPs）非法倾倒行为的智能市政固体废物管理方法。该系统通过街景摄像头与目标检测算法实现对倾倒垃圾的快速检测与监控。数据采集自印度特伦甘纳邦的桑加雷迪县。基于所构建的数据集，我们开展了一系列综合实验以评估多种目标检测模型的准确性与整体性能。具体而言，我们对YOLOv8、YOLOv10、YOLO11m及RT-DETR模型进行了深入评估。在这些模型中，YOLO11m在垃圾检测任务中取得了92.39%的最高准确率，证明了其在废物检测中的有效性。同时，该模型获得了0.91的mAP@50值，凸显了其高精度特性。这些结果证实了目标检测模型非常适用于GVP位置的垃圾倾倒事件监测与追踪。此外，该系统能有效捕捉垃圾处置模式，包括每小时、每日及每周的倾倒趋势，确保实现全天候的全面监控。