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An annotated water-filled, and dry potholes dataset for deep learning applications

Dib, Jihad, Sirlantzis, Konstantinos, Howells, Gareth (2023) An annotated water-filled, and dry potholes dataset for deep learning applications. Data in Brief, 48 . Article Number 109206. E-ISSN 2352-3409. (doi:10.1016/j.dib.2023.109206) (KAR id:101156)


Potholes have long posed a challenging risk to automated systems due to their random and stochastic shapes and the reflectiveness of their surface when filled with water, whether it is “muddy” water or clear water. This has formed a significant limitation to autonomous assistive technologies such as Electric-Powered Wheelchairs (EPWs), mobility scooters, etc. due to the risk potholes pose on the user’s well-being as it could cause severe falls and injuries as well as neck and back problems. Current research proved that Deep Leaning technologies are one of the most relevant solutions used to detect potholes due to the high accuracy of the detection. One of the main limitations to the datasets currently made available is the lack of photos describing water-filled, rabble-filled, and random coloured potholes. The purpose of our dataset is to provide the answer to this problem as it contains 713 high-quality photos representing 1152 manually-annotated potholes in different shapes, locations, colours, and conditions, all of which were manually-collected via a mobile phone and within different areas in the United Kingdom along with two additional benchmarking videos recorded via a dashcam.

Item Type: Article
DOI/Identification number: 10.1016/j.dib.2023.109206
Additional information: For the purpose of open access, the author has applied a CC BY public copyright licence (where permitted by UKRI, an Open Government Licence or CC BY ND public copyright licence may be used instead) to any Author Accepted Manuscript version arising
Uncontrolled keywords: potholes; dataset; pattern recognition; object localisation; image understanding; computer vision; deep learning; object detection; image processing; convolutional neural networks
Subjects: Q Science > Q Science (General) > Q335 Artificial intelligence
T Technology > TA Engineering (General). Civil engineering (General) > TA1637 Image processing
T Technology > TK Electrical engineering. Electronics. Nuclear engineering > TK7800 Electronics > TK7880 Applications of electronics > TK7882.P3 Pattern recognition systems
Divisions: Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Engineering and Digital Arts
Funders: Engineering and Physical Sciences Research Council (
Depositing User: Jihad Dib
Date Deposited: 07 May 2023 16:37 UTC
Last Modified: 04 Mar 2024 17:36 UTC
Resource URI: (The current URI for this page, for reference purposes)

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