International Research Journal of Education and Technology

To ensure safety, efficiency, and security, modern transportation systems rely on real-time traffic observation and detection. Using deep learning and computer vision techniques, this study suggests a new way to monitor and detect traffic in real-time. Using cutting-edge object detection techniques and convolutional neural networks (CNNs), the suggested system can reliably identify and follow moving objects in traffic camera footage. To tailor pre-trained convolutional neural network (CNN) models to the unique demands of traffic monitoring jobs, the system employs state-of-the-art methods like data augmentation and transfer learning. It also includes techniques for estimating crowd densities, detecting anomalies, and analyzing traffic flows, all of which contribute to better traffic management and better decisions. After conducting thorough experiments and evaluations on real-world traffic datasets, it has been found that the suggested methodology outperforms standard methods in terms of detection accuracy, speed, and scalability. This research adds to the growing body of knowledge on intelligent transportation systems by providing a dependable and effective method for monitoring and detecting traffic in real-time. It might be used for a variety of purposes, including public safety, congestion management, and traffic monitoring.