Paper Contents
Abstract
The "Garbage Detection and Reporting System" is an innovative approach designed to address the growing inefficiencies in urban waste management. Due to their slow response times and vulnerability to human error, traditional manual methods for identifying and collecting garbage are becoming less and less effective as cities grow. This project offers a quicker and more accurate way to detect and report waste by automating the process using computer vision and machine learning. The system can effectively identify different kinds of waste materials because OpenCV is used for real-time image and video processing. An easy-to-use platform where users can upload images or live video feeds to obtain comprehensive analytical reports is offered by a Flask-based web interface. The underlying machine learning model of the system is trained on a variety of datasets to guarantee dependable performance in a range of waste categories, lighting conditions, and weather scenarios. It is appropriate for deployment in busy urban areas because of its real-time processing capability, which guarantees low latency. The system improves operational efficiency, facilitates optimized scheduling, and permits data-driven decision-making in waste collection by decreasing reliance on manual labor and minimizing errors. Additionally, it facilitates targeted cleanup efforts by assisting in the identification of high-waste zones. In the future, the system has a great deal of potential for expansion in smart city ecosystems through scalability, future integration with IoT devices, and sophisticated deep learning algorithms.
Copyright
Copyright © 2025 Pranay Pohokar. This is an open access article distributed under the Creative Commons Attribution License.
