EfficientNet-B7 Enhanced Road Accident Detection Using CCTV Footage

In the Accident Events Detected by CCTV Camera dataset, a model combining deep learning techniques is created. The Accuracy Rate of the training reached almost 98.99 percent while for parameters such as RMSE it was 94.90 percent in validation phase, Tests showed its strong suitability to practical use. The dataset includes sets of situations that can occur in all sorts of different ways on roads, roadsides and road surfaces. This means that the model is able to handle numerous types of accident with minimal error. It is an efficient and effective model whose architecture is rooted in EfficientNet-B7 and deep convolutional neural network (CNNs), trained to mine the static and dynamic features effective in CCTV footage. During the training phase for instance hyperparameters should be adjusted wherever possible so the model can catch as much of the small accident-signaling nuances as feasible. Moreover, analysis of the interpretability of decisions made by this model produced by our research brings added transparency and trustworthiness in terms of operator, area and consumer. This accuracy proves the model’s ability to distinguish between normal road traffic incidents and possible accidents and so is a valuable tool for road safety measures that are proactive. The research contributes to, and underlines for, the trend toward using high and new technology means in prevention of traffic accidents as well as development of good reliability systems to clear up after consistencies.