Detect Objects in Images Using the YOLOv8 Neural Network
Zayn Saddique
Object Detection and Score Updating for Pickleball Manager Livestream
In the Pickleball Manager Livestream project, I implemented an advanced object detection system using the YOLOv8 neural network to enhance the viewing experience and streamline score updates during matches. This integration allows for real-time identification of players and balls, facilitating accurate and immediate scorekeeping.
Key Features
YOLOv8 Neural Network: Leveraging the capabilities of the YOLOv8 model, I achieved high accuracy in detecting objects such as players and pickleballs in live video feeds. YOLOv8’s speed and efficiency make it ideal for real-time applications, ensuring minimal latency during livestreams.
Model Training with OpenCV: To tailor the model to our specific requirements, I utilized OpenCV for data preprocessing and augmentation. This involved gathering training data, labeling it accurately, and optimizing the model to recognize various player movements and ball trajectories effectively.
Backend Implementation: The backend system is developed using both Flask and FastAPI, providing a robust architecture for handling requests and managing real-time data updates.
Flask serves as the core framework for managing application routes and basic functionalities.
FastAPI is integrated to handle high-performance requirements, particularly for real-time score updates and streaming data.
Workflow
Video Input: The live video feed from the pickleball matches is captured using a camera setup. This feed is processed in real-time to detect players and balls.
Object Detection: The YOLOv8 model analyzes each frame of the video feed, identifying the positions of players and the ball. The model outputs bounding boxes around detected objects, which are then used to track movements.
Score Updating: As the model detects specific events (e.g., ball in/out, player scoring a point), the backend systems in Flask and FastAPI update the match score accordingly. This is done through WebSocket connections, ensuring that viewers receive immediate updates on score changes without delays.
User Interface Integration: The updated scores and detected object information are sent to the frontend, where they are displayed in real-time for viewers, enhancing engagement and interactivity.
Conclusion
The integration of YOLOv8 for object detection, alongside Flask and FastAPI for backend management, has significantly improved the functionality of the Pickleball Manager Livestream. This system not only enhances the viewer experience by providing real-time updates but also ensures accurate scorekeeping, making it an invaluable tool for both players and fans alike.
Like this project
Posted Oct 15, 2024
I implemented YOLOv8 for real-time object detection in the Pickleball Manager livestream, using Flask and FastAPI for score updates and enhanced viewer engage
