This project demonstrates a state-of-the-art real-time object detection system built using the YOLOv8 framework. The system is capable of detecting objects in images and videos with high accuracy and speed, making it suitable for real-world applications like traffic monitoring and robotics.

• PlatformPython, Jupyter Notebook
• StackPyTorch, OpenCV, YOLOv8
• ModelYOLOv8 Object Detection Framework

The system is built on the following workflow:

1. Data Collection & Preparation

2. Model Training

3. Model Evaluation

4. Real-Time Prediction

The data preparation process involves structuring a dataset with images and corresponding labels. The labels follow the YOLO format, specifying bounding boxes and object classes. Tools like LabelImg are used to annotate images.

• Training Data:

  A custom dataset like Detect-Traffic-Sign-6 containing images of traffic signs.

• Validation Data:

  A separate dataset to evaluate model performance after training.

The YOLOv8 model is trained by feeding it batches of labeled images. The training process involves:

• Hyperparameter Tuning:

  Configuring parameters like learning rate, batch size, and epochs to optimize performance.

• Neural Network:

  YOLOv8’s architecture processes input images to predict bounding boxes and class labels.

After training, the model is evaluated using a validation dataset. Metrics likemAP (mean Average Precision), precision, and recall are used to assess performance.

• Visualization:

  Results are visualized with bounding boxes and labels for validation data.

The trained model is integrated with OpenCV to process real-time video streams. Each frame is analyzed to detect and classify objects on the fly.

• Webcam Integration:

  The system connects to a webcam to detect objects in real-time.

• Applications:

  Suitable for tasks like traffic monitoring, security systems, and robotics.