---
license: apache-2.0
task_categories:
  - object-detection
tags:
  - drone
  - uav
  - imav
  - robotics
  - autonomous-landing
  - helipad-detection
  - nectar-sdk
  - IMAV-2025
size_categories:
  - 1K<n<10K
pretty_name: IMAV 2025 Platform Detection Dataset
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/train-*
dataset_info:
  features:
    - name: image
      dtype: image
    - name: image_id
      dtype: int64
    - name: width
      dtype: int32
    - name: height
      dtype: int32
    - name: objects
      struct:
        - name: id
          sequence: int64
        - name: bbox
          sequence:
            sequence: float32
            length: 4
        - name: category
          sequence:
            class_label:
              names:
                '0': platform
        - name: area
          sequence: float64
---

# IMAV 2025 Platform Detection Dataset

Object detection dataset for landing platform detection in the IMAV 2025 Indoor Competition - Mission 4. The MAV must autonomously land on a moving 1m x 1m platform with an H-marking helipad, with optional smoke occlusion.

**Trained model:** [blackbeedrones/imav-2025-platform](https://huggingface.co/blackbeedrones/imav-2025-platform)

## Competition Context

The [16th International Micro Air Vehicle Conference and Competition (IMAV 2025)](https://femexrobotica.org/imav2025/) took place in San Andres 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.

## Mission 4: Land on Moving Platform with Smoke

The MAV must autonomously land on a moving platform:
- Platform size: 1m x 1m
- Lateral movement: up to 1m
- Max speed: 0.5 m/s
- Obstacle: Smoke machine (partial occlusion)

## Target Object

- Board: 1m x 1m square
- Outer circle: 0.85m (black stroke)
- Inner circle: 0.8m
- H marking: 0.6m height, 0.35m width, 0.075m stroke

## Dataset Structure

| Split | Images |
|-------|--------|
| train | 1043 |

**Total images:** 1043

**Classes:** `platform`

**Annotation format:** COCO (x_min, y_min, width, height)

## Usage

### Load with HuggingFace Datasets

```python
from datasets import load_dataset

dataset = load_dataset("blackbeedrones/imav-2025-platform-dataset")
example = dataset["train"][0]
print(example["objects"])  # {'bbox': [...], 'category': [...]}
```

### Visualize with bounding boxes

```python
import torch
from torchvision.ops import box_convert
from torchvision.utils import draw_bounding_boxes
from torchvision.transforms.functional import pil_to_tensor, to_pil_image

example = dataset["train"][0]
categories = dataset["train"].features["objects"].feature["category"]

boxes_xywh = torch.tensor(example["objects"]["bbox"])
boxes_xyxy = box_convert(boxes_xywh, "xywh", "xyxy")
labels = [categories.int2str(x) for x in example["objects"]["category"]]

to_pil_image(
    draw_bounding_boxes(
        pil_to_tensor(example["image"]),
        boxes_xyxy,
        colors="red",
        labels=labels,
    )
)
```

### Convert to COCO JSON (for training)

```python
import json
from datasets import load_dataset

dataset = load_dataset("blackbeedrones/imav-2025-platform-dataset", split="train")
categories = dataset.features["objects"].feature["category"]

coco = {
    "images": [],
    "annotations": [],
    "categories": [
        {"id": i, "name": n} for i, n in enumerate(categories.names)
    ],
}

ann_id = 0
for row in dataset:
    coco["images"].append({
        "id": row["image_id"],
        "width": row["width"],
        "height": row["height"],
        "file_name": f"{row['image_id']}.jpg",
    })
    row["image"].save(f"images/{row['image_id']}.jpg")

    for bbox, cat, area in zip(
        row["objects"]["bbox"],
        row["objects"]["category"],
        row["objects"]["area"],
    ):
        coco["annotations"].append({
            "id": ann_id,
            "image_id": row["image_id"],
            "category_id": cat,
            "bbox": bbox,
            "area": area,
            "iscrowd": 0,
        })
        ann_id += 1

with open("annotations.json", "w") as f:
    json.dump(coco, f)
```

### Train with Nectar SDK

```python
from nectar.ai.detection import Detector, TrainingConfig

detector = Detector("yolo11n.pt")
detector.load()
result = detector.train(TrainingConfig(
    dataset_path="path/to/converted/dataset",
    epochs=100,
    push_to_hub=True,
    hub_model_id="blackbeedrones/imav-2025-platform",
))
```

### Train with Ultralytics (YOLO format)

```python
from datasets import load_dataset
from pathlib import Path

dataset = load_dataset("blackbeedrones/imav-2025-platform-dataset")
categories = dataset["train"].features["objects"].feature["category"]

for split_name, split_data in dataset.items():
    img_dir = Path(f"yolo_dataset/images/{split_name}")
    lbl_dir = Path(f"yolo_dataset/labels/{split_name}")
    img_dir.mkdir(parents=True, exist_ok=True)
    lbl_dir.mkdir(parents=True, exist_ok=True)

    for row in split_data:
        fname = f"{row['image_id']}"
        row["image"].save(img_dir / f"{fname}.jpg")
        w, h = row["width"], row["height"]

        with open(lbl_dir / f"{fname}.txt", "w") as f:
            for bbox, cat in zip(row["objects"]["bbox"], row["objects"]["category"]):
                x_center = (bbox[0] + bbox[2] / 2) / w
                y_center = (bbox[1] + bbox[3] / 2) / h
                bw, bh = bbox[2] / w, bbox[3] / h
                f.write(f"{cat} {x_center} {y_center} {bw} {bh}\n")
```

## References

- [IMAV 2025](https://femexrobotica.org/imav2025/)
- [Rulebook](https://femexrobotica.org/imav2025/index.php/rulebook-imav-2025/)
- [Nectar SDK](https://github.com/Black-Bee-Drones/nectar-sdk)
- [Trained Model](blackbeedrones/imav-2025-platform)