YOLO Object Detection Web Application

Project Overview

This project is a web application designed for real-time object detection in videos using the state-of-the-art YOLOv8 model. The application leverages Streamlit to create an interactive user interface and OpenCV for efficient video processing. The purpose of this project is to demonstrate the capabilities of deep learning in computer vision, particularly in object detection tasks.

Features

• Interactive Web Interface: The application provides a user-friendly interface built with Streamlit, allowing users to upload video files for object detection.
• Real-Time Detection: Utilizing the YOLOv8 model, the application performs real-time object detection on each frame of the uploaded video.
• Dynamic Visualizations: The detected objects are highlighted with bounding boxes and class labels, making it easy to visualize the detection results directly on the video frames.
• Color Coding: Each detected object class is color-coded for better visualization and differentiation.

Technologies and Tools Used

• YOLOv8: A state-of-the-art object detection model that provides high accuracy and speed.
• Streamlit: A powerful tool for creating interactive web applications with Python.
• OpenCV: An open-source computer vision library used for video processing and frame manipulation.
• NumPy: A fundamental package for numerical computing in Python, used for array manipulation and operations.

Implementation Details

• Class List Loading: The class list for object detection is loaded from a file, ensuring that each detected object can be labeled correctly.
• Color Generation: Random colors are generated for each class to differentiate the detected objects visually.
• YOLOv8 Model Loading: The YOLOv8 model is loaded to perform the object detection.
• Video Upload and Processing: The application allows users to upload a video, which is then processed frame by frame for object detection.
• Detection and Visualization: For each frame of the video, the YOLOv8 model predicts the bounding boxes, class IDs, and confidence scores of the detected objects. These are then drawn on the frames using OpenCV.

Skills Demonstrated

• Machine Learning: Implementation and fine-tuning of the YOLOv8 model for object detection tasks.
• Python Programming: Writing efficient and modular code for video processing and web application development.
• Web Development: Building a user-friendly interface using Streamlit to interact with the machine learning model.
• Computer Vision: Applying techniques for real-time object detection and visualization using OpenCV.

Conclusion

This project showcases the powerful combination of deep learning and computer vision, providing an interactive platform for real-time object detection. It highlights the ability to integrate various technologies to create a comprehensive solution, demonstrating both technical skills and practical application of machine learning models.