Authors : Sanjay A. V.; Girichandran A. R.; Dr. G. Valarmathy; Desinhraja D.

Volume/Issue : Volume 11 - 2026, Issue 4 - April

Google Scholar : https://tinyurl.com/5bjtxr4h

Scribd : https://tinyurl.com/2c4jd6tf

DOI : https://doi.org/10.38124/ijisrt/26apr1955

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

Abstract : Road accidents have become very common these days because of rapid urbanization. In big cities where traffic is dense and bad driving is common, road accidents happen very frequently. People are dying even before receiving any medical help, just because their accident is not visible to the naked eye or not notified in time. In this paper, we discuss a real-time accident detection and alert system for automatically detecting accidents with the help of YOLOv8 and OpenCV. The system monitors the roads with the help of CCTV cameras. The system detects the accident in real-time by automatically processing the live videos. As soon as an accident happens the system takes the image and locates the camera position and notifies to concerned authorities through email by attaching the image and location of the accident. This system will help emergency staff to reach the location faster, and also make the city smart and safe.

References :

1. O. ElSahly and A. Abdelfatah, “A systematic review of traffic incident detection algorithms,” Sustainability, vol. 14, no. 22, p. 14859, Nov. 2022. MDPI
2. H. Yuan and G. Li, “A survey of traffic prediction: from spatio-temporal data to intelligent transportation,” Data Science and Engineering, vol. 6, pp. 63–85, 2021.
3. J. Zhang, K. Yang, and R. Stiefelhagen, “ISSAFE: improving semantic segmentation in accidents by fusing event-based data,” in Proc. IEEE Conference on Intelligent Vehi-cles/related symposium, Aug. 2020.
4. P. C. Sherimon, V. Sherimon, J. Joy, A. M. Kuruvilla, and G. Arundas, “Efficient deep learning methods for detecting road accidents by analysing traffic accident images,” Int. J. Computing, vol. 23, no. 3, 2023.
5. V. Adewopo and N. Elsayed, “Smart city transportation: deep learning ensemble approach for traffic accident detec-tion,” arXiv pre-print, Oct. 2023.
6. N. M. and Y. S. Shajitha, “Multi-modal traffic analysis: integrating time-series forecasting, accident prediction, and image classification,” arXiv pre-print, Apr. 2025.
7. J. Redmon, S. Divvala, R. Girshick, and A. Farhadi, “You only look once: Unified, real-time object detection,” in 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 779–788.
8. A. Krizhevsky, I. Sutskever, and G. E. Hinton, “Im-ageNet classification with deep convolutional neural net-works,” in Advances in Neural Information Processing Sys-tems (NIPS), 2012, pp. 1097–1105.
9. M. Chen, Y. Zhang, Y. Li, S. Mao, and V. C. Leung, “EMC: Emotion-aware mobile cloud computing in 5G,” IEEE Network, vol. 29, no. 2, pp. 32–38, 2015.
10. R. Vera-Amaro, M. E. Rivero-Angeles, and A. Luviano-Jua´rez, “Data collection schemes for accident and emergency monitoring using WSNs assisted by UAVs,” Sen-sors, vol. 20, no. 1, p. 262, 2020.