---
license: mit
pipeline_tag: object-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`:
```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:

```python
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

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