Intelligent vision based system for overtaking of vehicle

Overtaking of heavy vehicles, like trucks, has a significant risk due to limited visibility and potential collisions. This research work addresses the challenge by accurately detecting and tracking vehicles in real time, predicting overtaking decisions and providing information to the drivers for safer driving. In recent years, deep learning methods have shown a robust performance compared to traditional techniques, and transformed how vehicles are detected and counted in various scenarios. This research focuses on enhancing safety during overtaking on single-lane roads by developing a vehicle detection and counting system using the MobileNet-SSD deep learning model. Through meticulous testing on Common Objects in Context (COCO) dataset, this system achieves an average accuracy of 98.7% in detecting and counting vehicles, demonstrating its efficacy and reliability. These results demonstrate the potential of intelligent vision-based systems to significantly improve safety and traffic .