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In Romania, road infrastructure is classified based on traffic intensity into four types of streets: magistral (used for crossing the city), connection, collection, and local use. This study utilizes the TRIB crack dataset (Traffic Road Infrastructure from Bucharest crack dataset), which consists of high-quality images of various types of road cracks. The dataset can be effectively used for different computer vision tasks, such as classification, object detection, and more. To meet the diverse requirements of deep learning methods, the dataset includes images capturing different types of road cracks, such as longitudinal, transverse, block, and alligator cracks, as well as various artifacts like oil stains, road markings on asphalt, leaves, and more. The images were taken from a height of 100 centimeters above the road surface, resulting in a dataset of 137 RGB (red, green, blue) images. To make the images suitable for deep learning methods, they were divided into smaller images with a resolution of 256\u2009\u00d7\u2009256 pixels. Additionally, various image augmentation techniques were applied. During the splitting process, some images contained no cracks, while others included cracks. This resulted in the creation of two distinct subsets: one containing image with road cracks and another with images without cracks.<\/jats:p>","DOI":"10.1007\/s12145-025-01763-7","type":"journal-article","created":{"date-parts":[[2025,2,12]],"date-time":"2025-02-12T00:58:12Z","timestamp":1739321892000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["TRIB crack dataset: automatic recognition system for road cracks detection"],"prefix":"10.1007","volume":"18","author":[{"ORCID":"https:\/\/orcid.org\/0009-0008-6112-0838","authenticated-orcid":false,"given":"Dumitru","family":"Abrudan","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,2,12]]},"reference":[{"issue":"2","key":"1763_CR1","doi-asserted-by":"publisher","first-page":"917","DOI":"10.3906\/elk-1905-42","volume":"28","author":"A Akram","year":"2020","unstructured":"Akram A, Debnath R (2020) An automated eye disease recognition system from visual content of facial image using machine learning techniques. 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This has been replaced with the appropriate text.","order":6,"name":"change_details","label":"Change Details","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"251"}}