YOLO on Raspberry Pi, Jetson Nano, RDK X3 or RDK X5 Edge Devices with NCNN Real-Time Optimization

💻Code and Doc: https://kevinwoodrobotics.com/product... 🎁 Get FREE Robotics & AI Resources (Guide, Textbooks, Courses, Resume Template, Code & Discounts) – Sign up via the pop-up at https://kevinwoodrobotics.com/ 📝 Real-World Robotics Project Course Overview and Waitlist: https://forms.gle/xu4rfk7GWHCaZGBEA 🦾 Learn Robotics and AI: https://kevinwoodrobotics.com/product... 🤝 Consulting and Mentorship: https://kevinwoodrobotics.com/product... 👁️ Learn OpenCV: https://kevinwoodrobotics.com/product... 🤖 Learn ROS: https://kevinwoodrobotics.com/product... 🧠 Learn Computer Vision using AI: https://kevinwoodrobotics.com/product... 🖥️ Learn AI and Machine Learning: https://kevinwoodrobotics.com/product... 🛒 Amazon Store (My Top Picks for Robot Projects and More!): https://www.amazon.com/shop/kevinwood... ☕ Support My Channel: https://buymeacoffee.com/kevinwoodrob... ⚡️Things I Used and Mentioned in This Video: Raspberry Pi: https://amzn.to/44I2RFL Jetson Nano: https://amzn.to/4jTTFmc RDK x5: https://amzn.to/3GrBrKk Logitech Webcam: https://amzn.to/3GEZLs1 SD Card: https://amzn.to/3S6eVsO 5V 5A Power Supply: https://amzn.to/434sJKM Ready to take your AI and robotics projects to the next level? In this comprehensive tutorial, I'll show you step-by-step how to run YOLO (You Only Look Once) object detection with a standard Logitech USB camera on popular edge computing devices like the Raspberry Pi, the powerful RDK-X3, the AI-enhanced RDK-X5, and even the NVIDIA Jetson Nano. Stop the lag! You'll see firsthand the performance difference between unoptimized and optimized YOLO on these embedded systems. Learn the crucial optimization techniques needed to achieve significantly faster frames per second (FPS) – going from sluggish to near real-time object detection (achieving 3-4 FPS!). This is essential for any practical computer vision application on resource-constrained hardware. Here's what you'll learn: Running YOLO on Edge Devices: See it in action on Raspberry Pi, RDK-X3, RDK-X5, and Jetson Nano. Understanding Performance Bottlenecks: Why standard YOLO implementations struggle on embedded systems. YOLO Optimization for Speed: Discover the key steps to dramatically improve performance. Introducing NCNN for Edge Inference: Learn how to use the NCNN framework (developed by Tencent) to convert your PyTorch YOLO models for efficient deployment on edge devices. Step-by-Step NCNN Conversion: A simple two-line code demonstration in VS Code to convert your YOLO model. Working with Converted NCNN Models: How to load and run your optimized YOLO model. Visualizing Results with Supervision: Learn how to use the supervision Python library for easy and customizable object detection annotations (bounding boxes and labels). Exploring the RDK-X3 and RDK-X5 Boards: A detailed look at the hardware specifications, features (CPU, BPU/AI compute, GPU), and ports of these powerful edge AI development boards. RDK-X3 Camera Module: An overview of the 400W rolling shutter camera and how to connect it. RDK-X3 Case Assembly: A quick guide to assembling the protective metal case with heat sink functionality. Flashing the RDK-X3 Operating System: Learn how to prepare your SD card with the necessary OS image using Balena Etcher. Thanks for watching! If you found this video helpful, please like, subscribe and share:    / @kevinwoodrobotics   Sharing my referral link for when you order your Tesla. You’ll get $1,000 off the purchase of a Tesla product! https://ts.la/kevin145437 Social: Website: https://www.kevinwoodrobotics.com LinkedIn:   / kevinwoodrobotics   Instagram:   / kevinwoodrobotics   #YOLO #EdgeAI #RaspberryPi #JetsonNano #RDKX3 #RDKX5 #EmbeddedAI #RealTimeDetection #ComputerVision #AIOptimization #YOLOOptimization #NCNN #LogitechCamera #Ultralytics #RoboticsSoftware #ObjectDetection #PythonAI #OpenCV #AIonEdgeDevices #MachineLearning #AIProjects