Add dataset card and assets

README.md

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+---
+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: