README.md

---
license: apache-2.0
tags:
  - object-detection
  - yolo
  - yolov11
  - ultralytics
  - drone
  - uav
  - imav
  - robotics
  - autonomous-landing
  - helipad-detection
datasets:
  - custom
pipeline_tag: object-detection
model-index:
  - name: platform_yolov11n
    results:
      - task:
          type: object-detection
        metrics:
          - type: mAP@50
            value: 0.995
          - type: mAP@50-95
            value: 0.973
          - type: precision
            value: 0.996
          - type: recall
            value: 0.989
---

# IMAV 2025 Platform Detection - YOLOv11n

Platform detection model for **IMAV 2025 Indoor Competition - Mission 4**.

## Competition Context

The [16th International Micro Air Vehicle Conference and Competition (IMAV 2025)](https://femexrobotica.org/imav2025/) took place in San Andrés Cholula, Puebla, Mexico. The competition theme was **"Search and Rescue"**, inspired by Mexico's seismic activity and the need for micro air vehicles in disaster response scenarios.

## Target Object

![Landing Platform](platform.png)

**Platform Specifications:**
- Board: 1m × 1m square
- Outer circle: Ø 0.85m (black stroke)
- Inner circle: Ø 0.8m
- H marking: 0.6m height, 0.35m width, 0.075m stroke

## Mission 4: Land on Moving Platform with Smoke

The MAV must autonomously land on a moving platform:

| Parameter | Value |
|-----------|-------|
| Platform size | 1m × 1m |
| Lateral movement | up to 1m |
| Max speed | 0.5 m/s |
| Obstacle | Smoke machine (partial occlusion) |

**Scoring:**

| Task | Points |
|------|--------|
| No landing | 0 |
| Landing (stationary) | 2 |
| Landing (moving platform) | +3 |
| Landing (with smoke) | +3 |

## Performance

| Metric | Value |
|--------|-------|
| mAP@50 | 0.995 |
| mAP@50-95 | 0.973 |
| Precision | 0.996 |
| Recall | 0.989 |

### Training Curves

![Training Results](train/results.png)

### Confusion Matrix

![Confusion Matrix](train/confusion_matrix.png)

### Validation Predictions

![Validation Predictions](train/val/val_batch0_labels.jpg)

## Model Formats

| Format | File | Use Case |
|--------|------|----------|
| PyTorch | `platform_yolov11n.pt` | Training, fine-tuning |
| ONNX | `platform_yolov11n.onnx` | Cross-platform inference |
| TensorRT | `platform_yolov11n.engine` | Jetson Orin Nano Super |

## Training Configuration

| Parameter | Value |
|-----------|-------|
| Base model | yolo11n.pt |
| Epochs | 100 |
| Image size | 640×640 |
| Batch | Auto |
| Optimizer | Auto |
| LR | 0.01 → 0.01 (cosine) |
| Augmentation | Mosaic, RandAugment |
| Dropout | 0.05 |

Full config: [`train/args.yaml`](train/args.yaml)

## Usage

### mirela-sdk

```python
from mirela_sdk.ai.detection import Detector

detector = Detector("blackbeedrones/imav-2025-platform:best.pt")
detector.load()

result = detector.detect(image, conf=0.5)
for det in result:
    print(f"Platform: {det.confidence:.2f} at {det.center}")
```

### Ultralytics

```python
from ultralytics import YOLO

model = YOLO("best.pt")
results = model.predict(image, conf=0.5)
```

## References

- [IMAV 2025](https://femexrobotica.org/imav2025/)
- [Rulebook](https://femexrobotica.org/imav2025/index.php/rulebook-imav-2025/)
- [mirela-sdk](https://github.com/blackbeedrones/mirela-sdk)