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Title	Integrating Generative Adversarial Networks And Convolutional Neural Networks For Enhanced Traffic Accidents Detection And Analysis
ID_Doc	31999
Authors	Xi Z.; Liu X.; Liu Y.; Cai Y.; Zheng Y.
Year	2025
Published	IEEE Transactions on Intelligent Transportation Systems
DOI	http://dx.doi.org/10.1109/TITS.2025.3573172
Abstract	Accident detection using Closed Circuit Television (CCTV) footage is one of the most imperative features for enhancing transport safety and efficient traffic control. To this end, this research addresses the issues of supervised monitoring and data deficiency in accident detection systems by adapting excellent deep learning technologies. The motivation arises from rising statistics in the number of car accidents worldwide; this calls for innovation and the establishment of a smart, efficient and automated way of identifying accidents and calling for help to save lives. Addressing the problem of the scarcity of data, the presented framework joins Generative Adversarial Networks (GANs) for synthesizing data and Convolutional Neural Networks (CNN) for model training. Video frames for accidents and non-accidents are collected from YouTube videos, and we perform resizing, image enhancement and image normalisation pixel range adjustments. Three models are used: CNN, Fine-tuned Convolutional Neural Network (FTCNN) and Vision Transformer (VIT) worked best for detecting accidents from CCTV, obtaining an accuracy rate of 94% and 95%, while the CNN model obtained 88%. Such results show that the proposed framework suits traffic safety applications due to its high real-time accident detection capabilities and broad-scale applicability. This work lays the foundation for intelligent surveillance systems in the future for real-time traffic monitoring, smart city framework, and integration of intelligent surveillance systems into emergency management systems. © 2000-2011 IEEE.
Author Keywords	closed circuit television (CCTV); Convolutional neural network (CNN); generative adversarial network (GANs); vision transformer (VIT)

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