---
license: mit
pipeline_tag: object-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 below shows the normalized detection accuracy across different classes. Key insights include:

Drone Detection: 85% accuracy, 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% accuracy, 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
Install Dependencies
Install the required packages listed in requirements.txt:

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

Example Results
Class	True Positive Rate	Common Misclassifications
Drone	85%	Background
Airplane	99%	None
Helicopter	67%	Bird
Bird	68%	Helicopter
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:

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