Here’s a sample publication document for a YOLOv8 model designed for flag detection and classification, written in Markdown:

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YOLOv8-Based Flag Detection and Classification

Introduction

Flag detection and classification play a significant role in various applications, including international events, border security, and educational purposes. Using the cutting-edge YOLOv8 (You Only Look Once version 8) model, we have developed a robust system for real-time detection and classification of national flags.

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Key Features

1. State-of-the-Art Model

YOLOv8: The latest version in the YOLO series, offering faster and more accurate object detection.

2. High Accuracy

Capable of identifying over 200 flags with precision, even in challenging conditions like partial occlusion or low lighting.

3. Real-Time Processing

Processes input from cameras at 30+ FPS, ensuring real-time flag detection in dynamic environments.

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Model Training Pipeline

Dataset Preparation

1. Data Sources:

Collected images of national flags from online repositories and public datasets.

Augmented data to include various angles, lighting, and partial views.

2. Dataset Structure:

/dataset
├── /train
│ ├── img1.jpg
│ ├── img2.jpg
│ └── ...
├── /val
│ ├── img1.jpg
│ ├── img2.jpg
│ └── ...
└── /test
├── img1.jpg
├── img2.jpg
└── ...

3. Annotation Format:
   Used YOLO annotation format for bounding boxes:

<class_id> <x_center> <y_center>

Model Training

Framework: Ultralytics YOLOv8

Hardware: NVIDIA RTX 3090 GPU

Hyperparameters:

epochs: 100
batch_size: 16
learning_rate: 0.001
img_size: 640

Training Command:

yolo task=detect mode=train data=flag_dataset.yaml model=yolov8n.pt epochs=100 imgsz=640

Evaluation

Metrics:

mAP (Mean Average Precision): 0.92

Precision: 0.95

Recall: 0.90

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Deployment

1. Model Inference

The trained model is deployed using the YOLOv8 Python API:

from ultralytics import YOLO

Load the model

model = YOLO("best.pt")

Perform inference

results = model.predict(source="test_image.jpg", conf=0.5)

Display results

results.show()

2. Real-Time Detection

Integrated with a webcam for real-time flag detection:

model.predict(source=0, conf=0.5, show=True)

3. Classification Output

The model outputs:

Flag Name (e.g., "Egypt", "USA")

Confidence Score

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Results

Sample Detections:

The model successfully detects and classifies multiple flags in a real-world environment.

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Applications

1. Sports Events:
   Automatically identify flags during international tournaments.

2. Education:
   Use in interactive tools for teaching geography and cultural studies.

3. Surveillance:
   Enhance border security by identifying national flags on vehicles or equipment.

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Future Work

Expand the dataset to include regional and organizational flags.

Improve robustness for flags in motion or harsh environmental conditions.

Integrate the system into mobile applications and embedded devices.

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Conclusion

Our YOLOv8-based flag detection and classification model demonstrates high accuracy and efficiency, paving the way for innovative applications in diverse fields.

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Contact Us

For inquiries or collaboration opportunities:

Email: ac.flight1214@gmail.com