Copyright © 2026 Authors retain the copyright of this article. This article is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

@article{172457,
        author = {Kusuma S and Nagaraja M and Meghana S and Prathiksha L and Sowmya V},
        title = {Waste Management Using CCTV Monitoring and Data Analytics},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {8},
        pages = {3230-3235},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=172457},
        abstract = {The rapid urbanization and population growth have led to increased waste mismanagement, particularly in restricted areas. This project aims to address the issue of unauthorized waste disposal using a robust AI-powered surveillance system. The system integrates YOLOv5 for object detection to identify both individuals and trash within a restricted area. If a person is detected, a Convolutional Neural Network (CNN) is utilized for facial recognition to ascertain their identity. In cases where facial recognition fails, the system switches to vehicle number plate detection to identify the perpetrator. This dual-layered identification approach ensures that individuals or vehicles responsible for illegal waste disposal can be identified effectively. Once the offender is identified through facial recognition or vehicle number plate detection, an automated fine system is triggered to discourage such activities.},
        keywords = {},
        month = {January},
        }