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YOLOv8 Object Detection on Jetson Nano | Real-Time AI Demonstration
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**YOLOv8 Object Detection on Jetson Nano | Real-Time AI Demonstration**
Welcome to my project showcase! In this video, I demonstrate the implementation of YOLOv8 for real-time object detection on the NVIDIA Jetson Nano. Watch as I take you through the steps of setting up the environment, installing necessary dependencies, and running YOLOv8 to detect objects in real-time.
**Project Overview:**
- **Author:** Darshan Anand
- **Institution:** Dayananda Sagar University
- **Field of Study:** Pre-final Year CSE-AIML Student
**Installation Steps:**
1. Install Jetpack 4.6 on Jetson Nano
2. Update and install dependencies
3. Clone the YOLOv8 repository
4. Create and activate a Python 3.8 virtual environment
5. Update Python packages
6. Download and install pre-built PyTorch and TorchVision packages
7. Install YOLOv8 Python package
8. Execute object detection
**Performance Metrics:**
- YOLOv8n: 6.1 FPS (Detect), 4.2 FPS (Segment)
- YOLOv8s: 3.1 FPS (Detect), 2.2 FPS (Segment)
- YOLOv8m: 1.3 FPS (Detect), 0.96 FPS (Segment)
- YOLOv8l: 0.77 FPS (Detect), 0.61 FPS (Segment)
- YOLOv8x: 0.48 FPS (Detect), 0.38 FPS (Segment)
**Conclusion:**
This project demonstrates the viability of deploying advanced AI models on edge devices, opening up numerous possibilities for real-time, on-device intelligence in various applications. The setup and installation were straightforward, and the YOLOv8 model performed remarkably well given the hardware constraints of the Jetson Nano.
**Future Work:**
- Optimization of the YOLOv8 model for better performance
- Exploring other versions of YOLOv8 for speed and accuracy trade-offs
- Integration with other sensors and systems for enhanced capabilities
**GitHub Repository:**
For detailed instructions, scripts, and project files, visit my GitHub repository: [GitHub Repository Link]
**Contact:**
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