{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,18]],"date-time":"2026-06-18T18:36:33Z","timestamp":1781807793233,"version":"3.54.5"},"reference-count":43,"publisher":"MDPI AG","issue":"13","license":[{"start":{"date-parts":[[2024,6,25]],"date-time":"2024-06-25T00:00:00Z","timestamp":1719273600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"China National Geological survey project Construction of Big Data Intelligent Prediction System for Mineral Resources","award":["DD20243267"],"award-info":[{"award-number":["DD20243267"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Rock image classification represents a challenging fine-grained image classification task characterized by subtle differences among closely related rock categories. Current contrastive learning methods prevalently utilized in fine-grained image classification restrict the model\u2019s capacity to discern critical features contrastively from image pairs, and are typically too large for deployment on mobile devices used for in situ rock identification. In this work, we introduce an innovative and compact model generation framework anchored by the design of a Feature Positioning Comparison Network (FPCN). The FPCN facilitates interaction between feature vectors from localized regions within image pairs, capturing both shared and distinctive features. Further, it accommodates the variable scales of objects depicted in images, which correspond to differing quantities of inherent object information, directing the network\u2019s attention to additional contextual details based on object size variability. Leveraging knowledge distillation, the architecture is streamlined, with a focus on nuanced information at activation boundaries to master the precise fine-grained decision boundaries, thereby enhancing the small model\u2019s accuracy. Empirical evidence demonstrates that our proposed method based on FPCN improves the classification accuracy mobile lightweight models by nearly 2% while maintaining the same time and space consumption.<\/jats:p>","DOI":"10.3390\/s24134127","type":"journal-article","created":{"date-parts":[[2024,6,26]],"date-time":"2024-06-26T09:29:33Z","timestamp":1719394173000},"page":"4127","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["An Optimization Method for Lightweight Rock Classification Models: Transferred Rich Fine-Grained Knowledge"],"prefix":"10.3390","volume":"24","author":[{"given":"Mingshuo","family":"Ma","sequence":"first","affiliation":[{"name":"Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9677-4152","authenticated-orcid":false,"given":"Zhiming","family":"Gui","sequence":"additional","affiliation":[{"name":"Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhenji","family":"Gao","sequence":"additional","affiliation":[{"name":"Integrated Natural Resources Survey Center, CGS, No. 55 Yard, Honglian South Road, Xicheng District, Beijing 100055, China"},{"name":"Technology Innovation Center of Geological Information Engineering of Ministry of Natural Resources, Beijing 100055, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bin","family":"Wang","sequence":"additional","affiliation":[{"name":"Integrated Natural Resources Survey Center, CGS, No. 55 Yard, Honglian South Road, Xicheng District, Beijing 100055, China"},{"name":"Technology Innovation Center of Geological Information Engineering of Ministry of Natural Resources, Beijing 100055, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2024,6,25]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"331","DOI":"10.1016\/S0886-7798(02)00106-2","article-title":"Classification as a tool in rock engineering","volume":"18","author":"Stille","year":"2003","journal-title":"Tunn. 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