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{199003,
        author = {Priyanka Manoj Shinde and Anjali Suryavanshi and Vishal Bhadane and Piyush Gangarde},
        title = {Smart Street Light System with Road Safety Integration},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {11},
        pages = {11407-11412},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=199003},
        abstract = {This research presents a smart street-light system integrated with real-time accident detection using the YOLOv8 deep-learning model. The proposed solution combines IoT hardware with advanced computer vision to identify road accidents quickly and accurately. Live video from an ESP32-CAM module is processed through a Python-based YOLOv8 model to detect collisions and send instant alerts. The system also includes automatic street-light control using proximity sensors and fault monitoring for safer and more energy-efficient city infrastructure. Experimental results show fast response, high detection accuracy, and strong potential for deployment in modern smart-city environments.
The integration of deep learning, computer vision, and IoT technologies makes the system suitable for real-time deployment in smart city environments. The proposed approach offers a cost-effective, scalable, and efficient solution for improving road safety, reducing accident response time, and enhancing intelligent traffic management systems.},
        keywords = {Smart Street Light, Road Accidents, Accident Detection, Computer Vision, Machine Learning, Deep Learning, YOLOv8, Real-time Monitoring, Intelligent Transportation},
        month = {April},
        }