yolov8m_flying_objects_detection

yolov8m_flying_objects_detection

yolov8m_flying_objects_detection is a deep learning model designed to detect various flying objects, including drones, airplanes, helicopters, and birds. Based on the YOLOv8 architecture, this model provides a strong balance of speed and accuracy, making it suitable for real-time aerial surveillance and monitoring applications.

Model Summary

This model has been trained to identify the following classes:

• Drone (UAV copter)
• Airplane
• Helicopter
• Bird
• Background (no object)

Classes and Objects

The model has been trained to detect and classify the following types of flying objects:

1. Drones
   • DJI Matrice 200
   • DJI Phantom 2
   • DJI Phantom 3
   • Shahed
2. Airplanes
   • Airbus A220
   • Airbus A220 (with stowed landing gear)
   • Airbus A380
   • Boeing 787
   • Boeing 787 (with stowed landing gear)
3. Helicopters
   • Bell 407
   • Robinson R44
4. Birds
   • Chayka (Seagull)
   • Golub (Pigeon)
5. Background
   • Areas with no relevant objects.

This breakdown provides more specific information on each class, helping users understand the diversity of objects the model can detect.

Confusion Matrix Analysis

The confusion matrix above shows the normalized detection accuracy across different classes. Key insights include:

• Drone Detection: 85% accurate, with occasional misclassifications as background.
• Airplane Detection: Excellent accuracy of 99%.
• Helicopter Detection: Correctly identified 67% of the time, with some confusion with birds.
• Bird Detection: 68% accurate, with some misclassifications as helicopters.
• Background: Some non-object areas are occasionally detected as objects.

Applications

This model is particularly useful in scenarios where real-time identification of airborne objects is essential. Potential applications include:

• Airport Surveillance: Detecting drones and birds to prevent collisions and ensure safety.
• Military and Security Operations: Monitoring restricted airspaces for unauthorized drones or other aerial vehicles.
• Wildlife Monitoring: Identifying bird movements to support ecological studies and prevent hazards.

Model Usage

To use the model, follow these steps:

Install Dependencies

Install the required packages listed in requirements.txt:

pip install -r requirements.txt

Run Inference

Load the model and run inference on images or video frames using the sample inference.py script:

from yolov8 import YOLO
model = YOLO("yolov8m_fly_obj_detection.pt")
results = model.predict("image.jpg")

The model outputs bounding boxes for each detected object, along with their respective class labels and confidence scores.

Example Results

Limitations

• Class Confusion: Some confusion exists between similar classes (e.g., helicopters and birds).
• Background Misclassification: Non-object areas may occasionally be misclassified as objects.

License

This model is released under the MIT License. Feel free to use, modify, and distribute it, but please provide proper attribution.

Citation

If you use this model in your work, please consider citing it as follows:

@model{yolov8m_flying_objects_detection,
  title={YOLOv8m Flying Object Detection},
  author={Javvanny},
  year={2024},
  howpublished={\url{https://huggingface.co/Javvanny/yolov8m_flying_objects_detection}},
}