---
pretty_name: BFD-UAV2K
task_categories:
- object-detection
language:
- en
tags:
- uav
- building-facade
- defect-detection
- object-detection
- computer-vision
size_categories:
- 1K<n<10K
---

# BFD-UAV2K: UAV-Based Building Facade Defect Detection

**BFD-UAV2K** is a realistic UAV-based building facade defect dataset and benchmark for detecting facade defects in full-frame inspection images. The dataset targets practical UAV inspection settings where defects are often small, sparse, low-contrast, and embedded in complex facade backgrounds.

<p align="center">
  <img src="assets/pipeline.png" alt="BFD-UAV2K pipeline and benchmark framework" width="92%">
</p>

## Highlights

- **2,000 full-frame UAV images** collected from real-world building facade inspection campaigns.
- **Fixed benchmark split:** 1,600 training images, 200 validation images, and 200 test images.
- **Single detection class:** `defect`, covering cracking, spalling, and hollow-drum-like facade damage.
- **Realistic inspection scenes:** marble panels, windows, concrete/cement surfaces, pipes, cables, air-conditioning units, and other facade-side interference.
- **Multiple detector families:** YOLO, RT-DETR, Faster R-CNN, and Cascade R-CNN are evaluated under a unified benchmark protocol.
- **Deployment-oriented analysis:** speed, localization quality, low-data behavior, and hard-case failure patterns are analyzed together.

## Dataset Overview

| Item | Value |
|---|---:|
| Images | 2,000 |
| Positive / negative images | 912 / 1,088 |
| Defect instances | 2,664 boxes |
| Split | 1,600 / 200 / 200 |
| Annotation format | YOLO TXT + COCO JSON |
| Class | `defect` |
| UAV platform | DJI Mini 3 |
| Capture range | 2-30 m wall-facing UAV inspection geometry |

<p align="center">
  <img src="assets/positive_negative_examples.png" alt="Positive and negative examples" width="82%">
</p>

## Benchmark Models

The benchmark compares representative detectors from three major detection families:

| Family | Models |
|---|---|
| One-stage detectors | YOLOv5mu, YOLOv8m, YOLOv11m |
| Transformer detector | RT-DETR-L |
| Two-stage detectors | Faster R-CNN, Cascade R-CNN |

## Main Results

| Model | mAP@0.5 | mAP@0.5:0.95 | Precision | Recall | F1 | Time (ms/img) |
|---|---:|---:|---:|---:|---:|---:|
| YOLOv5mu | **0.7193** | 0.3631 | 0.8043 | 0.6491 | 0.7184 | **6.00** |
| YOLOv8m | 0.6710 | 0.3347 | 0.7753 | 0.5965 | 0.6743 | 6.65 |
| YOLOv11m | 0.6799 | 0.3416 | **0.8059** | 0.5827 | 0.6763 | 7.00 |
| RT-DETR-L | 0.7116 | **0.3811** | 0.7781 | **0.6772** | **0.7241** | 14.84 |
| Faster R-CNN | 0.5750 | 0.2810 | 0.5752 | 0.6526 | 0.6115 | 12.41 |
| Cascade R-CNN | 0.5740 | 0.2960 | 0.5735 | 0.6491 | 0.6090 | 25.77 |

YOLOv5mu provides the strongest accuracy-speed trade-off, while RT-DETR-L achieves the best strict-IoU localization score and F1 score. Two-stage detectors are more sensitive to repetitive facade textures and weak defect boundaries in this benchmark.

<p align="center">
  <img src="assets/qualitative_comparison.png" alt="Qualitative comparison across baseline detectors" width="96%">
</p>

## Low-Data Behavior

The benchmark also evaluates model behavior under reduced training sizes: 100, 200, 400, and 800 samples. YOLO-family detectors show stable improvement as more data becomes available, while RT-DETR-L is more sensitive in very low-data regimes but becomes highly competitive with more labeled samples.

<p align="center">
  <img src="assets/data_scale_curves.png" alt="Detector performance under different training data sizes" width="90%">
</p>

## Hard Cases

BFD-UAV2K includes practical facade inspection challenges that expose clear differences between detector families:

| Hard-case pattern | Ratio |
|---|---:|
| Long cracks with extreme aspect ratio | 10% |
| Defect-background texture overlap | 20% |
| Multi-anomaly scenes with missed detections | 30% |
| Uneven concrete coating / non-uniform background | 30% |
| Complex structural background interference | 10% |

<p align="center">
  <img src="assets/failure_cases.png" alt="Failure case analysis" width="96%">
</p>

## Repository Contents

```text
.
|-- README.md
|-- assets/
|   |-- pipeline.png
|   |-- positive_negative_examples.png
|   |-- qualitative_comparison.png
|   |-- data_scale_curves.png
|   `-- failure_cases.png
|-- data/
|-- annotations/
|-- labels/
`-- scripts/
```

The public dataset release is hosted in this Hugging Face dataset repository. The companion GitHub repository is available at [HanboYangC/BFD-UAV2K](https://github.com/HanboYangC/BFD-UAV2K).

## Citation

```bibtex
@article{bfduav2k,
  title   = {BFD-UAV2K: A Realistic Dataset and Benchmark for Unmanned Aerial Vehicle-Assisted Building Facade Defect Detection},
  author  = {},
  journal = {},
  year    = {}
}
```

## License

License information will be added with the official public release.

## Organizer

Kang Yang, email: kyang16866@gmail.com
Ruoyu Chen, email: