Implementing Deep Learning and CNNs for Vehicle Detection to Alleviate Highway Traffic Congestion

Traffic jams are a common problem in urban areas, and detecting them in real time can help alleviate congestion and improve traffic flow. This project, propose CNN-based method for detecting traffic jams from live video feeds. Our system involves capturing video streams from cameras placed in strategic locations and processing them using a deep-learning model. The study aims to develop a system for detecting traffic jams using (CNN) Convolutional Neural Networks. The system analyzes real-time traffic images to identify congested areas and provides insights to traffic management authorities for timely intervention. The study examines the accuracy of the system by testing it on a dataset of traffic videos. We evaluated our system on a dataset of traffic videos and obtained an average accuracy 89.05.11%, the error average 0.10%., and average F-score 89.61% indicating that our approach can effectively detect traffic jams. Our experiments show that our CNN-based approach outperforms traditional methods of traffic jam detection and can provide accurate and real-time information to drivers and traffic management systems.