Portfolio

Overview:

This project focuses on tracking vehicles and calculating their speed using the YOLOv8 model. With a dataset of 4680 images, the system tracks cars and trucks, calculating speed based on distance and time as vehicles cross designated lines on the screen. The model achieves a mean average precision (mAP) of 0.764.

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Key Features:

1. YOLOv8 for Vehicle Detection:

The YOLOv8l model is used to detect vehicles such as cars and trucks in real time. This model offers high-speed detection and tracking, which is essential for calculating vehicle speeds as they move through the camera’s field of view.

2. Real-Time Speed Calculation: The system tracks the movement of vehicles across pre-defined lines in the video feed. Speed is calculated by measuring the time it takes for a vehicle to cross the distance between two lines, converting pixel displacement into real-world speed.

3. Dataset of 4680 Images: The model was trained on a dataset consisting of 4680 images with multiple vehicle classes, enhancing the system’s ability to detect a variety of vehicles, including cars and trucks, in different traffic scenarios.

4. Accuracy (mAP of 0.764): After 50 epochs of training, the model achieves a mean average precision (mAP) of 0.764, ensuring reliable detection and tracking of vehicles, which is crucial for accurate speed estimation.

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Links

• GitHub
• YouTube