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{172382,
        author = {Abdul Moin and Kummara Anil Kumar and Keerthana B A and Sandhya Rani M H and Priyanka G},
        title = {Metro Platform Overcrowding Detection And Notification System Based On Image Processing},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {8},
        pages = {2961-2965},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=172382},
        abstract = {The increasing population and demand for public transportation have led to significant overcrowding on metro platforms, which can cause delays, safety hazards, and discomfort for passengers. To address this issue, this project proposes an innovative solution based on real-time image processing using the YOLOv5 (You Only Look Once) object detection model, integrated with a Telegram bot for automated notifications. The system leverages high-definition cameras installed on metro platforms to capture images of the crowd density. YOLOv5 is used to detect and count the number of people on the platform, providing an accurate estimate of crowd size. Based on the detected density, the system classifies whether the platform is overcrowded, moderately crowded, or clear, triggering an automated notification to a predefined Telegram channel.},
        keywords = {},
        month = {January},
        }