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Enhanced rock recognition via EVSS-integrated YOLO11: A deep learning approach for precise geological classification.

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Title: Enhanced rock recognition via EVSS-integrated YOLO11: A deep learning approach for precise geological classification.
Authors: Zhao F; State Key Laboratory of Geohazard Prevention and Geoenvironment Protection, Chengdu University of Technology, Chengdu, China.; BIM Technology Application Industry Research Center, Sichuan Communication Surveying and Design Institute, Chengdu, China., Leng X; State Key Laboratory of Geohazard Prevention and Geoenvironment Protection, Chengdu University of Technology, Chengdu, China.; College of Computer Science and Cyber Security, Chengdu University of Technology, Chengdu, China., Zhu M; BIM Technology Application Industry Research Center, Sichuan Communication Surveying and Design Institute, Chengdu, China., Luo S; College of Computer Science and Cyber Security, Chengdu University of Technology, Chengdu, China., Huang J; State Key Laboratory of Geohazard Prevention and Geoenvironment Protection, Chengdu University of Technology, Chengdu, China.
Source: PloS one [PLoS One] 2026 Apr 24; Vol. 21 (4), pp. e0341862. Date of Electronic Publication: 2026 Apr 24 (Print Publication: 2026).
Publication Type: Journal Article
Journal Info: Publisher: Public Library of Science Country of Publication: United States NLM ID: 101285081 Publication Model: eCollection Cited Medium: Internet ISSN: 1932-6203 (Electronic) Linking ISSN: 19326203 NLM ISO Abbreviation: PLoS One Subsets: MEDLINE
Database: MEDLINE Ultimate
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  Data: <searchLink fieldCode="AU" term="%22Zhao+F%22">Zhao F</searchLink>; State Key Laboratory of Geohazard Prevention and Geoenvironment Protection, Chengdu University of Technology, Chengdu, China.; BIM Technology Application Industry Research Center, Sichuan Communication Surveying and Design Institute, Chengdu, China.<br /><searchLink fieldCode="AU" term="%22Leng+X%22">Leng X</searchLink>; State Key Laboratory of Geohazard Prevention and Geoenvironment Protection, Chengdu University of Technology, Chengdu, China.; College of Computer Science and Cyber Security, Chengdu University of Technology, Chengdu, China.<br /><searchLink fieldCode="AU" term="%22Zhu+M%22">Zhu M</searchLink>; BIM Technology Application Industry Research Center, Sichuan Communication Surveying and Design Institute, Chengdu, China.<br /><searchLink fieldCode="AU" term="%22Luo+S%22">Luo S</searchLink>; College of Computer Science and Cyber Security, Chengdu University of Technology, Chengdu, China.<br /><searchLink fieldCode="AU" term="%22Huang+J%22">Huang J</searchLink>; State Key Laboratory of Geohazard Prevention and Geoenvironment Protection, Chengdu University of Technology, Chengdu, China.
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  Data: <searchLink fieldCode="JN" term="%22101285081%22">PloS one</searchLink> [PLoS One] 2026 Apr 24; Vol. 21 (4), pp. e0341862. <i>Date of Electronic Publication: </i>2026 Apr 24 (<i>Print Publication: </i>2026).
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        Value: 10.1371/journal.pone.0341862
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