ArticleScientific data2025
A Large-Scale Image Repository for Automated Pavement Distress Analysis and Degradation Trend Prediction.
Article in Scientific data, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 2 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
2 citing papers in PubMed.
- Real-world road damage dataset with potholes, cracks, and maintenance holes.Scientific reports · 2026Article
- A Large-Scale Image Repository for Automated Pavement Distress Analysis and Degradation Trend Prediction.Scientific data · 2025Article
Corrections and comments
- Erratum issued
Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In recent years, automated detection technologies for large-scale pavement distress have become a focal point of research in the transportation sector. With the rapid advancement of deep learning technologies, data-driven artificial intelligence recognition algorithms have gradually emerged as the industry mainstream. The effectiveness of such algorithms largely depends on the reliability and quantity of the samples. However, existing datasets exhibit significant shortcomings in terms of sample size, category diversity, and support for distress tracking. In this study, a large-scale image dataset was meticulously constructed. This dataset includes 51012 road images for pavement distress identification and 8928 images for long-term tracking of pavement distress. Using this dataset, six mature object detection algorithms were trained and evaluated, with the results demonstrating the performance of these algorithms. To the best of our knowledge, this is the first large-scale pavement distress dataset that includes long-term tracking of pavement distress, providing reliable data support for dynamic tracking and monitoring of pavement distress as well as for optimizing road maintenance strategies.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.