metadata
license: apache-2.0
tags:
- object-detection
- yolo
- yolov11
- ultralytics
- drone
- uav
- imav
- robotics
- autonomous-landing
- helipad-detection
datasets:
- blackbeedrones/imav-2025-platform-dataset
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) 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
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
Confusion Matrix
Validation Predictions
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
Usage
Nectar SDK
from nectar.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
from ultralytics import YOLO
model = YOLO("best.pt")
results = model.predict(image, conf=0.5)