Abstract
India has around one and a half lakh persons die due to road accidents per year. One of the main reasons for this number is no timely availability of help. Automatic accident detection can shorten the response time of rescue agencies and vehicles around accidents to improve rescue efficiency and traffic safety level. The ability to detect and track vehicle can be used in applications like monitoring road accidents. The proposed system uses YOLOv5 deep learning algorithm to detect the vehicles from the real time CCTV surveillance video. The primary focus of the system is to build a model that detects various class of vehicle by using custom dataset. The dataset consists of 1000 images with various condition such as rainfall, low visibility, luminosity, and weather conditions. The proposed framework uses YOLOv5 to detect vehicle with improved efficiency in real time object detection system. This model can further be extended to analyse and classify accidents using 3D Convolutional Neural Network based on the severity of the accident and to alert the nearest hospital.
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References
Pillai, M.S., Khari, M.: Real-time image enhancement for an automatic automobile accident detection through CCTV using deep learning. Soft Comput. 25, 11929–11940 (2021). https://doi.org/10.1007/s00500-021-05576-w
Nancy, P., Dilli Rao, D., Babuaravind, G., Bhanushree, S.: Highway accident detection and notification using machine learning. IJCSMC 9, 168–176 (2020)
Yu, Y., Xu, M., Gu, J.: Vision-based traffic accident detection using sparse spatio-temporal features and weighted extreme learning machine. IET Intell. Transp. Syst. 13, 1417–1428 (2019)
Tian, D., Zhang, C., Duan, X., Wang, X.: An automatic car accident detection method based on cooperative vehicle infrastructure systems. IEEE Access 7, 127453–127463 (2019)
Diwan, A., Gupta, V., Chadha, C.: Accident detection using mask R-CNN. IJMTST 7, 69–72 (2021)
Yadav, D.K., Renu, Ankita, Anjum, I.: Accident detection using deep learning. IJECCE (2020)
Lee, K.B., Shin, H.S.: An application of a deep learning algorithm for automatic detection of unexpected accidents under bad CCTV monitoring conditions tunnels. IEEE (2019)
Singh, C., Mohan, K.: Deep spatio-temporal representation for detection of road accidents. IEEE (2019)
Yao, Y., Xu, M., Wang, Y., Crandall, D.J., Atkins, E.M.: Unsupervised traffic accident detection in first-person videos. IEEE (2020)
Mothe, S., Teja, A.S., Kakumanu, B., Tata, R.K.: A model for assessing the nature of car crashes using convolutional neural networks. Int. J. Emerg. Trends Eng. Res. 8, 859–863 (2020)
Rajesh, G., Benny, A.R : A deep learning based accident detection system. In: ICCSP (2020)
Lu, Z., Zhou, W., Zhang, S., Wang, C.: A new video-based crash detection method: balancing speed and accuracy using a feature fusion deep learning framework. IJAT (2020)
Robles-Serrano, S., Sanchez-Torres, G., Branch-Bedoya, J.: Automatic detection of traffic accidents from video using deep learning techniques. MDPI (2021)
Sindhu, V.S.: Vehicle identification from traffic video surveillance using YOLOv4. IEEE (2021)
Jahagirdar, S.: Automatic accident detection techniques using CCTV surveillance videos: methods, data sets and learning strategies. IJEAT 9, 2249–8958 (2020)
Mothe, S., Teja, A.S., Kakumanu, B., Tata, R.K.: A model for assessing the nature of car crashes using convolutional neural networks. IEEE (2020)
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Amala Ruby Florence, J., Kirubasri, G. (2022). Accident Detection System Using Deep Learning. In: Kalinathan, L., R., P., Kanmani, M., S., M. (eds) Computational Intelligence in Data Science. ICCIDS 2022. IFIP Advances in Information and Communication Technology, vol 654. Springer, Cham. https://doi.org/10.1007/978-3-031-16364-7_23
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DOI: https://doi.org/10.1007/978-3-031-16364-7_23
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