用GAN生成数据+CNN检测，提升交通事故识别准确率。
Integrating Generative Adversarial Networks and Convolutional Neural Networks for Enhanced Traffic Accidents Detection and Analysis
- 用GAN合成事故视频数据，缓解训练数据不足问题。
- 融合CNN与GAN的框架实现95%事故检测准确率。
- 适合智能交通、智慧城市及应急管理系统应用。
利用闭路电视（CCTV）视频进行事故检测是提升交通安全性与高效交通管理的关键功能。针对现有系统中监督监控困难与数据不足的问题，本研究引入深度学习技术。基于全球车祸数量上升的背景，提出结合生成对抗网络（GAN）生成数据与卷积神经网络（CNN）进行模型训练的框架。从YouTube收集事故与非事故视频帧，经尺寸调整、图像增强与像素归一化处理。对比三种模型：标准CNN、微调卷积神经网络（FTCNN）与视觉变换器（VIT），其中FTCNN与VIT分别达到94%与95%的准确率，而标准CNN为88%。结果表明该框架具备高实时性与广泛适用性，适用于未来智能监控系统、智慧城市建设及应急管理体系集成。
原文摘要 · Abstract (English)
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.
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