To explain the method for real-time object detection using YOLOv8

Real-time Object Detection with YOLOv8

1. Setup Environment2. Install YOLOv83. Prepare the Model4. Load and Configure5. Real-time Processing

Install Essentials

Ensure Python, pip, OpenCV, PyTorch, and YOLOv8 dependencies are installed.

Use pip or download from GitHub to install YOLOv8.

Download Weights

Get pre-trained YOLOv8 weights from GitHub or official sources.

Setup Video Input

Load model weights and configure webcam using OpenCV.

Capture and process frames in a loop for detection.

6. Post-Processing

Interpret model output: bounding boxes, class IDs, and confidence scores. Filter by confidence threshold.

7. Display Results

Draw bounding boxes and labels on frames. Display using OpenCV.

8. Optimization

Use GPU for acceleration. Adjust input size for speed.

9. Deployment

Consider containers for scalable deployment beyond development.