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.

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

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.

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.

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%

Repository Contents

.
|-- 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.

Citation

@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: