Deep Learning-Based Detection of Traffic Accidents Using CNN and VGG16 on Accident and Foggy Image Datasets

Detection of traffic accidents is a key part of intelligent transportation systems, providing real-time analysis in order to improve safety on the road and assist in the logistics and management of the traffic system. It's based on a study which introduces a new CNN --based deep learning framework at the VGG16 architecture. The model is trained on a varied dataset consisting of images from accident zones, non-accident images, and foggy images, with some classified as potential accident zones due to reduced visibility. Utilizing the feature extraction capabilities of CNN and the pre-trained VGG16 model, we intend to precisely classify accident occurrences across diverse environmental conditions, including low-visibility situations. This method underscores the significance of deep learning in the analysis of intricate road scenarios and the capability of automated traffic monitoring systems to enhance decision-making and response times in accident-prone regions. Our model demonstrates strong efficacy in differentiating between accident and non-accident scenarios, even under foggy conditions, rendering it a valuable asset for real-time traffic monitoring applications.