TamAko783/Unified_Road_Defect_Dataset

Unified Road Defect Dataset

A merged, YOLO-format road-defect detection dataset that combines RDD-2022 (primary, ground-level, 6 countries) with two supplementary aerial/drone datasets — UAV-PDD2023 (China) and RoadDamageVision (China + Spain) — into a single 4-class CRDDC schema.

This is a derived dataset. It re-packages and re-labels images from three independently published sources. All credit for the underlying images and original annotations belongs to their respective authors (see Licensing & Attribution). If you use this dataset you must cite all three source datasets below.

Classes (4-class CRDDC)

id	code	name
0	D00	Longitudinal Crack
1	D10	Transverse Crack
2	D20	Alligator Crack
3	D40	Pothole

Layout (Ultralytics YOLO)

data/
  images/train/  images/val/
  labels/train/  labels/val/   # <class> <cx> <cy> <w> <h>  (normalized)
rdd_merged.yaml                 # Ultralytics data config (nc=4)
STATS.md                        # per-class / per-source / per-country counts

Every file is source-prefixed (rdd_…, uav_…, rdv_…) so per-source metrics can be reported.

How the sources were merged

Source class	→ unified	rationale
RDD-2022 D00/D10/D20/D40	D00/D10/D20/D40	primary schema
RDD-2022 other (D11/D43/D44/D50…)	dropped	not in CRDDC 4-class
UAV-PDD2023 LC/TC/AC/PH	D00/D10/D20/D40	direct map
UAV-PDD2023 OC (oblique) / RP (repair)	dropped	no clean CRDDC equivalent
RoadDamageVision D00/D10/D20/D40	D00/D10/D20/D40	direct map (same D-codes)
RoadDamageVision Repair / Block Crack	dropped	no CRDDC 4-class equivalent

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Licensing & Attribution

This derived dataset is released for research purposes. The three sources were published under their own terms — verify and comply with each on its original host before redistribution or commercial use. The combined release inherits the most restrictive of the source terms.

1. RDD-2022 — primary

Multi-national Road Damage Dataset (CRDDC'2022), 47,420 images, 6 countries.

• Source: figshare 21431547 · sekilab/RoadDamageDetector
• License (per host): Creative Commons — verify on figshare before reuse.

@article{arya2022rdd2022,
  title   = {RDD2022: A multi-national image dataset for automatic road damage detection},
  author  = {Arya, Deeksha and Maeda, Hiroya and Kumar Ghosh, Sanjay and Toshniwal, Durga and Sekimoto, Yoshihide},
  journal = {Geoscience Data Journal},
  year    = {2022},
  doi     = {10.1002/gdj3.260}
}
@misc{arya2022rdd2022arxiv,
  title  = {RDD2022: A multi-national image dataset for automatic road damage detection},
  author = {Arya, Deeksha and Maeda, Hiroya and Kumar Ghosh, Sanjay and Toshniwal, Durga and Sekimoto, Yoshihide},
  year   = {2022}, eprint = {2209.08538}, archivePrefix = {arXiv}
}

2. UAV-PDD2023 — supplement (aerial)

2,439 UAV images, 11,154 instances, China, captured at 30 m.

• Source: Zenodo 8214118 (DOI 10.5281/zenodo.8214118) · Data in Brief 2023.
• License (per host): verify on Zenodo before reuse.

@article{uavpdd2023,
  title   = {UAV-PDD2023: A benchmark dataset for pavement distress detection based on UAV images},
  journal = {Data in Brief},
  year    = {2023},
  doi     = {10.1016/j.dib.2023.109692}
}
@dataset{uavpdd2023_zenodo,
  title     = {UAV-PDD2023},
  year      = {2023},
  publisher = {Zenodo},
  doi       = {10.5281/zenodo.8214118}
}

3. RoadDamageVision — supplement (aerial/drone, China + Spain)

Drone-captured road-surface images, manually annotated with bounding boxes for six defect types (D00, D10, D20, D40, Repair, Block Crack); 7,647 instances, D40 potholes most common (mostly the Spanish subset). COCO format.

• Source: Mendeley Data ypm4h4z25c v3 (DOI 10.17632/ypm4h4z25c.3).
• License: CC BY 4.0 (per Mendeley Data) — free to share/adapt with attribution.
• Authors: Luis Augusto Silva Zendron, Valderi Reis Quietinho Leithardt.

@dataset{roaddamagevision_mendeley,
  title     = {RoadDamageVision: Annotated Dataset of Road Damage Images},
  author    = {Silva Zendron, Luis Augusto and Leithardt, Valderi Reis Quietinho},
  year      = {2026},
  publisher = {Mendeley Data},
  version   = {3},
  doi       = {10.17632/ypm4h4z25c.3}
}

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Citing this compilation

Please cite all three source datasets above. You may additionally reference this compilation by its Hugging Face repo URL. The maintainer of this compilation claims no ownership over the underlying images or original labels.

Unified Road Defect Dataset - Statistics

Total images: 30,186 | Total instances: 75,765

Instances per class

id	class	train	val	total
0	Longitudinal Crack (D00)	27,114	4,807	31,921
1	Transverse Crack (D10)	16,358	2,992	19,350
2	Alligator Crack (D20)	9,941	1,769	11,710
3	Pothole (D40)	10,881	1,903	12,784

Images per source

source	train	val	total
RDD-2022	20,215	3,552	23,767
UAV-PDD2023	2,017	353	2,370
RoadDamageVision	3,445	604	4,049

Manhole hard-negative backgrounds: train 0, val 0

Images per country / view

country/view	images
Japan	7,900
United_States	4,805
China_Spain_UAV	4,049
India	3,223
Norway	2,914
China_UAV	2,370
China_MotorBike	1,934
China_Drone	1,919
Czech	1,072

Image size (sampled 800)

• width: min 512 median 640 max 4040
• height: min 512 median 640 max 2044