Deep Learning for Real-Time Traffic Intrusion Detection in Cloud Environments: A Security-Driven Approach

Abstract

We check CNN's ability to extract meaningful patterns and insights from complex traffic images. The fixed layers of CNN are specific to spatial data because of their ability to capture local properties through a hierarchical structure. By taking advantage of the CNN architecture such as VGGNet and the resane, we demonstrate their ability to treat video streams, detection of vehicles and pedestrians, identify traffic volumes and predict potential events based on visual signals.

We discover the use of real -time data flow for further decision -making for traffic management. The study emphasizes the function of traffic data for traffic data, training of CNN models and implementation of video-based traffic incident detection. The results suggest that CNN -s provide high accuracy and efficiency in traffic analysis compared to traditional data vision techniques, and provide better scalability for large urban data sets. Especially in image and video processing, Traditional Neural Networks (CNNs) have become a significant resource for examining spatial information. This article examines the application of CNN for spatial data analysis, with this paper traffic images and video fee. As urbanization increases, the management of traffic systems has become an important challenge for smart cities. Skilled traffic monitoring and analysis can reduce traffic stops, increase safety and optimize urban transport systems. Traditional traffic analysis techniques are often little dealing with real -time data, which leads to disabilities and exceptional opportunities for adaptation.