Real-Time Smart Bin Overflow Monitoring with YOLOv7

Urban waste management has emerged as a critical challenge, especially with the rising population density in cities. Overflowing garbage bins contribute to environmental degradation, unsanitary conditions, and public health hazards. Traditional methods of monitoring and managing waste are time-consuming, inefficient, and reliant on manual inspection, often leading to delayed waste collection. The YOLOv7 (You Only Look Once) object detection model is used in this proposed system for building a deep learning-oriented system for the detection and control of overflowing garbage containers. The system processes images and real-time video streams to identify bins that are overflowing and in need of immediate collection. Further integrate this model into a web application that allows users to report complaints, view bin locations, and track the status of garbage collection. A publicly accessible dataset was used to train the proposed system, and performance metrics like recall, precision, and mAP (mean average precision) were used to measure its impact. With a precision of 0.882 and a recall of 0.863, the YOLOv 7 model showed exceptional accuracy in identifying overflowing bins. Furthermore, the web application’s integration enhances the effectiveness of urban waste collection systems by offering an interface that is easy to handle for both residents and waste management administrators. The implementation of this system can significantly reduce the delay in waste collection and improve the overall hygiene and cleanliness of cities, making it a scalable and cost-effective solution for urban waste management.