{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,8]],"date-time":"2025-11-08T22:56:28Z","timestamp":1762642588397,"version":"3.41.2"},"reference-count":29,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2021,9,21]],"date-time":"2021-09-21T00:00:00Z","timestamp":1632182400000},"content-version":"vor","delay-in-days":263,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100003457","name":"Guilin University of Technology","doi-asserted-by":"publisher","award":["GLUTQD2017003"],"award-info":[{"award-number":["GLUTQD2017003"]}],"id":[{"id":"10.13039\/501100003457","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Computational Intelligence and Neuroscience"],"published-print":{"date-parts":[[2021,1]]},"abstract":"<jats:p>Rock classification is a significant branch of geology which can help understand the formation and evolution of the planet, search for mineral resources, and so on. In traditional methods, rock classification is usually done based on the experience of a professional. However, this method has problems such as low efficiency and susceptibility to subjective factors. Therefore, it is of great significance to establish a simple, fast, and accurate rock classification model. This paper proposes a fine\u2010grained image classification network combining image cutting method and SBV algorithm to improve the classification performance of a small number of fine\u2010grained rock samples. The method uses image cutting to achieve data augmentation without adding additional datasets and uses image block voting scoring to obtain richer complementary information, thereby improving the accuracy of image classification. The classification accuracy of 32 images is 75%, 68.75%, and 75%. The results show that the method proposed in this paper has a significant improvement in the accuracy of image classification, which is 34.375%, 18.75%, and 43.75% higher than that of the original algorithm. It verifies the effectiveness of the algorithm in this paper and at the same time proves that deep learning has great application value in the field of geology.<\/jats:p>","DOI":"10.1155\/2021\/5779740","type":"journal-article","created":{"date-parts":[[2021,9,22]],"date-time":"2021-09-22T00:20:06Z","timestamp":1632270006000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":18,"title":["Research on Classification of Fine\u2010Grained Rock Images Based on Deep Learning"],"prefix":"10.1155","volume":"2021","author":[{"given":"Yong","family":"Liang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1829-6658","authenticated-orcid":false,"given":"Qi","family":"Cui","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xing","family":"Luo","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhisong","family":"Xie","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2021,9,21]]},"reference":[{"key":"e_1_2_10_1_2","doi-asserted-by":"publisher","DOI":"10.1109\/access.2020.2982017"},{"key":"e_1_2_10_2_2","first-page":"163","article-title":"Feasibility study of deep learning algorithm applied to rock image processing","volume":"15","author":"Jian G.","year":"2016","journal-title":"Software Guide"},{"key":"e_1_2_10_3_2","first-page":"116","article-title":"Study on rock image classification based on convolution neural network","volume":"32","author":"Jian G.","year":"2017","journal-title":"Journal of Xi\u2032an Shiyou University (Natural Science Edition)"},{"key":"e_1_2_10_4_2","first-page":"3244","article-title":"Artificial intelligence identification of ore mineral under microscope based on deep learning algorithm","volume":"34","author":"Xu S.","year":"2018","journal-title":"Acta Petrologica Sinica"},{"key":"e_1_2_10_5_2","first-page":"333","article-title":"Automatic identification and classification in lithology based on deep learning in rock images","volume":"34","author":"Zhang Y.","year":"2018","journal-title":"Acta Petrologica Sinica"},{"key":"e_1_2_10_6_2","doi-asserted-by":"publisher","DOI":"10.1007\/s12145-019-00433-9"},{"key":"e_1_2_10_7_2","first-page":"178","article-title":"Mineral composition analysis of rock image based on deep learning feature extraction","volume":"27","author":"Bai L.","year":"2018","journal-title":"China Mining Magazine"},{"key":"e_1_2_10_8_2","doi-asserted-by":"crossref","unstructured":"GuojianC.andPeisongL. 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