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VEHICLE COUNTING USING DEEP LEARNING
Vehicle counting is an important task for traffic analysis and management. Object detection techniques using deep learning models have shown great success in accurately detecting and counting vehicles in real-time. In this project, we propose a vehicle counting system using the YOLO (You Only Look Once) v8 model. YOLO v8 is a state-of-the-art object detection model that can accurately detect objects in real-time with high precision and recall. We fine-tuned the model on a large dataset of vehicle images and used it to count the number of vehicles in a given scene. The proposed system achieved high accuracy and efficiency, making it suitable for real-time vehicle counting applications. The experimental results demonstrate that the proposed approach outperforms existing methods in terms of accuracy and speed. Our approach achieves high accuracy in vehicle detection and counting, making it a promising solution for real-world traffic management systems.