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Road Damage Detection datasets

archive 2025-07-28

3 datasets carry the task tag "Road Damage Detection" (the task itself: Road Damage Detection), ordered by the archive's paper count. Page 1 of 1: 3 shown of 3. Facet routes are this site's own (the archive records the tag string, not a page).

The archive holds 12,214 dataset rows; 12,172 are listed. 6 are withheld from every listing and count here as vandalised before snapshot (6 with contact-centre spam in the title, 0 with a spam description on a row that has no homepage, no paper and no papers counted; none with more than 1 paper, 0 with a benchmark), listed in withheld.json; 1 listed row carries a vandalised description, withheld on its page. This gate never withholds a row with a homepage or a paper that resolves, and a clean description; the content rules below withhold a row whose name is spam whatever else it carries. The gate is a phrase list: these are the rows it caught, not a claim that the rest is clean. Before that gate, the site's content rules withhold 36 more rows (invite-code, gambling, travel-booking, contact-centre and similar spam in the name or on a row with nothing real behind it); they have no page and are listed in withheld.json.

Filter 51 task tags shown of 3,717, by dataset count; the full filter by modality, task and language is on /datasets

Road Damage Detection datasets 1–3 of 3

RDD-2020 (Road Damage Dataset 2020)
The Road Damage Dataset 2020 (RDD-2020) Secondly is a large-scale heterogeneous dataset comprising 26620 images collected from multiple countries using smartphones.
4 papers · 0 benchmarks
Pothole Mix (Pothole Mix Semantic Segmentation Dataset for Road Damage Detection and Segmentation)
This dataset for the semantic segmentation of potholes and cracks on the road surface was assembled from 5 other datasets already publicly available, plus a very small addition of segmented images on our part.
2 papers · 1 benchmark
NPO (Negative and Positive Obstacles)
The dataset is recorded with an on-vehicle ZED stereo camera in both urban and rural environments The dataset contains various lighting conditions, such as normal lights, large-area shadows, dim lights, and sun glare.
1 paper · 1 benchmark

Paper counts and descriptions are the archive's, frozen 2025-07-28; no citation counts, no stars, no trending. Sorting by "most cited" or "newest" was a live-site feature the archive does not carry.