{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,26]],"date-time":"2026-06-26T11:45:48Z","timestamp":1782474348910,"version":"3.54.5"},"reference-count":58,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2023,3,17]],"date-time":"2023-03-17T00:00:00Z","timestamp":1679011200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Hubei Province Key Research and Development Plan","award":["2021BAA194"],"award-info":[{"award-number":["2021BAA194"]}]},{"name":"Hubei Province Key Research and Development Plan","award":["Guike AB21075009"],"award-info":[{"award-number":["Guike AB21075009"]}]},{"DOI":"10.13039\/501100017691","name":"Guangxi Key Research and Development Plan","doi-asserted-by":"publisher","award":["2021BAA194"],"award-info":[{"award-number":["2021BAA194"]}],"id":[{"id":"10.13039\/501100017691","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100017691","name":"Guangxi Key Research and Development Plan","doi-asserted-by":"publisher","award":["Guike AB21075009"],"award-info":[{"award-number":["Guike AB21075009"]}],"id":[{"id":"10.13039\/501100017691","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Due to the shortage of defect samples and the high cost of labelling during the process of hot-rolled strip production in the metallurgical industry, it is difficult to obtain a large quantity of defect data with diversity, which seriously affects the identification accuracy of different types of defects on the steel surface. To address the problem of insufficient defect sample data in the task of strip steel defect identification and classification, this paper proposes the Strip Steel Surface Defect-ConSinGAN (SDE-ConSinGAN) model for strip steel defect identification which is based on a single-image model trained by the generative adversarial network (GAN) and which builds a framework of image-feature cutting and splicing. The model aims to reduce training time by dynamically adjusting the number of iterations for different training stages. The detailed defect features of training samples are highlighted by introducing a new size-adjustment function and increasing the channel attention mechanism. In addition, real image features will be cut and synthesized to obtain new images with multiple defect features for training. The emergence of new images is able to richen generated samples. Eventually, the generated simulated samples can be directly used in deep-learning-based automatic classification of surface defects in cold-rolled thin strips. The experimental results show that, when SDE-ConSinGAN is used to enrich the image dataset, the generated defect images have higher quality and more diversity than the current methods do.<\/jats:p>","DOI":"10.3390\/s23063216","type":"journal-article","created":{"date-parts":[[2023,3,17]],"date-time":"2023-03-17T05:36:01Z","timestamp":1679031361000},"page":"3216","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":20,"title":["Steel Strip Defect Sample Generation Method Based on Fusible Feature GAN Model under Few Samples"],"prefix":"10.3390","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0471-0280","authenticated-orcid":false,"given":"Cancan","family":"Yi","sequence":"first","affiliation":[{"name":"Key Laboratory of Metallurgical Equipment and Control Technology (Wuhan University of Science and Technology), Ministry of Education, Wuhan 430081, China"},{"name":"Hubei Key Laboratory of Mechanical Transmission and Manufacturing Engineering (Wuhan University of Science and Technology), Wuhan 430081, China"},{"name":"Precision Manufacturing Institute (Wuhan University of Science and Technology), Wuhan 430081, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qirui","family":"Chen","sequence":"additional","affiliation":[{"name":"Key Laboratory of Metallurgical Equipment and Control Technology (Wuhan University of Science and Technology), Ministry of Education, Wuhan 430081, China"},{"name":"Hubei Key Laboratory of Mechanical Transmission and Manufacturing Engineering (Wuhan University of Science and Technology), Wuhan 430081, China"},{"name":"Precision Manufacturing Institute (Wuhan University of Science and Technology), Wuhan 430081, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Biao","family":"Xu","sequence":"additional","affiliation":[{"name":"Key Laboratory of Metallurgical Equipment and Control Technology (Wuhan University of Science and Technology), Ministry of Education, Wuhan 430081, China"},{"name":"Hubei Key Laboratory of Mechanical Transmission and Manufacturing Engineering (Wuhan University of Science and Technology), Wuhan 430081, China"},{"name":"Precision Manufacturing Institute (Wuhan University of Science and Technology), Wuhan 430081, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tao","family":"Huang","sequence":"additional","affiliation":[{"name":"Key Laboratory of Metallurgical Equipment and Control Technology (Wuhan University of Science and Technology), Ministry of Education, Wuhan 430081, China"},{"name":"Hubei Key Laboratory of Mechanical Transmission and Manufacturing Engineering (Wuhan University of Science and Technology), Wuhan 430081, China"},{"name":"Precision Manufacturing Institute (Wuhan University of Science and Technology), Wuhan 430081, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,3,17]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"753","DOI":"10.1016\/j.jmsy.2021.05.008","article-title":"A review on recent advances in vision-based defect recognition towards industrial intelligence","volume":"62","author":"Gao","year":"2022","journal-title":"J. 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