| --- |
| 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: |
|
|
