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Real-Time Object Detection using YOLOv8 and Python

Introduction

In this project, I implemented real-time object detection using the YOLOv8 architecture. My goal was to detect objects in both images and video streams efficiently, leveraging the power of deep learning. YOLOv8 was chosen for its balance between accuracy and speed.

Video Demonstration

You can watch a demonstration of the results Watch the video

Key Components

  • YOLOv8: Utilized the YOLOv8 model for object detection due to its optimized performance.
  • Python & Jupyter Notebooks: Employed Python for scripting and Jupyter notebooks for experimentation.
  • OpenCV for Video Processing: Integrated for real-time video analysis.
  • Model Inference: Performed object detection using pre-trained YOLOv8 weights on both images and video streams.

Steps

1. Dataset Preparation

Collected and processed data for testing the object detection model. The images were resized and normalized to match the input requirements of YOLOv8.

2. Model Selection

I selected the YOLOv8n variant for its lightweight architecture, suitable for real-time applications with limited computational resources.

3. Inference on Images and Video

The model was tested on a series of images and video streams, applying real-time detection and labeling. Detection results included bounding boxes, class labels, and confidence scores.

4. Results

The system successfully identified and labeled objects in real time, proving its efficiency in both static and video-based inputs.

Challenges

  • Balancing Accuracy and Speed: Ensuring high accuracy without sacrificing real-time performance.
  • Handling Various Object Sizes: Managing detection for both large and small objects in different contexts.
  • Image Quality: Detecting objects in varying conditions such as low light and motion blur.

Conclusion

This project showcases the ability of YOLOv8 to perform real-time object detection with high accuracy, even in dynamic video streams. It can be applied to a variety of fields, including surveillance and real-time analytics.

Future Improvements

  • Fine-tuning the model for specific use cases such as pedestrian or vehicle detection.
  • Enhancing performance under challenging conditions like low light and occlusions.
  • Implementing tracking across multiple frames to improve detection consistency.

Example Usage

Inference on an Image:

from ultralytics import YOLO

# Load model and image
model = YOLO('yolov8n.pt')
results = model('path/to/image.jpg')

# Display results
results.show()