ArticleData in brief2022
Dataset of road surface images with seasons for machine learning applications.
Article in Data in brief, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- BDRoad-Sense: A benchmark dataset for road surface detection and safety enhancement.Data in brief · 2026Article
- An unmanned aerial vehicle captured dataset for railroad segmentation and obstacle detection.Scientific data · 2024Article
- Exploring the natural pothole dataset generated by the abrasion and cavitation effects of river water on rocks.Data in brief · 2024Article
- UAV-PDD2023: A benchmark dataset for pavement distress detection based on UAV images.Data in brief · 2023Article
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Road surface monitoring plays a vital role in ensuring safety and comfort for the various road users, from pedestrians to drivers. Furthermore, this information is useful for the maintenance of the roads. The road condition deteriorates due to volatile weather. Thus the main objective of the proposed paper is to create an image dataset of the road surface for two seasons, i.e. summer and rainy. Accordingly, we created road surface images for different roads such as paved and unpaved roads. These folders consist of two subfolders for Rainy and Summer potholes. The dataset consists of 8484 images and 10 videos. This dataset is highly useful for machine learning experts working in the field of automatic vehicle controlling and road surface monitoring.
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Registered trials
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