Object Detection and Tracking with YOLO
Dive into the world of computer vision with this beginner-friendly, project-based course. Learn how to leverage the powerful YOLO (You Only Look Once) architecture to build real-time AI applications from scratch. Through hands-on exercises, you will master everything from running pre-trained models and creating custom datasets to implementing robust object tracking. By the end of this journey, you will have the practical skills to build and deploy your own intelligent vision systems.
Dipercaya oleh Perusahaan Terkemuka
Tentang Kursus Ini
Dive into the world of computer vision with this beginner-friendly, project-based course. Learn how to leverage the powerful YOLO (You Only Look Once) architecture to build real-time AI applications from scratch. Through hands-on exercises, you will master everything from running pre-trained models and creating custom datasets to implementing robust object tracking. By the end of this journey, you will have the practical skills to build and deploy your own intelligent vision systems.
Silabus Kursus
01 Introduction to Computer Vision and the YOLO Ecosystem
Introduction to Computer Vision and the YOLO Ecosystem
- The fundamentals of Computer Vision: Object Detection vs. Image Classification.
- The evolution and architecture of YOLO (Why it's fast and accurate).
- Setting up your development environment (Python, OpenCV, and Ultralytics library).
- Running your very first YOLO inference on a sample image.
02 Mastering Pre-Trained Models
Mastering Pre-Trained Models
- Understanding the output: Bounding boxes, class labels, and confidence scores.
- Processing different media types: Images, Videos, and Live Webcams.
- Customizing the output visualization using OpenCV.
- Mini-Project: Creating a real-time face and person detector using a webcam feed.
03 Data Collection and Annotation
Data Collection and Annotation
- The importance of high-quality data in Machine Learning.
- Sourcing public datasets (Kaggle, Roboflow Universe).
- Best practices for image annotation using tools like LabelImg or Roboflow.
- Understanding the YOLO dataset format (images, labels, and the
yamlconfiguration).
04 Training a Custom YOLO Model
Training a Custom YOLO Model
- Preparing your dataset for training (Train, Valid, Test splits).
- Configuring training hyperparameters (Epochs, Batch Size, Image Size).
- Training the model using cloud GPUs (Google Colab).
- Reading and interpreting training metrics (Loss graphs and mean Average Precision/mAP).
05 Advanced Object Tracking
Advanced Object Tracking
- The limitations of standard Object Detection in video streams.
- Introduction to multi-object tracking algorithms (ByteTrack and BoT-SORT).
- Assigning and maintaining unique IDs for detected objects across multiple frames.
- Handling occlusion (when objects are temporarily hidden).
06 Combining AI with Spatial Logic (OpenCV)
Combining AI with Spatial Logic (OpenCV)
- Defining Regions of Interest (ROI) and creating masking zones.
- Drawing virtual boundaries and lines on video frames.
- Mathematical logic for computer vision: Calculating the center points of bounding boxes.
- Determining the direction of a moving object based on coordinate changes.
07 Model Export and Optimization
Model Export and Optimization
- Best practices for saving and loading your custom weights (
best.pt). - Exporting YOLO models to different formats (ONNX, TensorRT, or TensorFlow Lite) for faster inference.
- Tips for optimizing processing speed on standard CPUs.
08 Capstone Project Assembly
Capstone Project Assembly
- Integrating custom detection models with tracking algorithms.
- Merging spatial logic (from Module 6) with the AI tracking pipeline.
- Troubleshooting common integration issues.
- Finalizing the codebase and exporting the final project output.
Proyek Akhir
Smart Traffic Monitoring and Vehicle Counting System
As the final project, you will build a comprehensive, real-time traffic monitoring application. Starting with raw CCTV highway footage, you will deploy a YOLO model to identify different types of vehicles. You will then implement a tracking algorithm to follow each vehicle's trajectory and apply spatial logic to count them as they cross a customized "virtual tripwire" drawn on the screen. This project mimics real-world smart city applications and serves as a powerful portfolio piece.
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