{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,19]],"date-time":"2026-02-19T10:27:13Z","timestamp":1771496833611,"version":"3.50.1"},"reference-count":31,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2026,2,19]],"date-time":"2026-02-19T00:00:00Z","timestamp":1771459200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Yunnan Key Laboratory of Water Security","award":["20254916CE340051"],"award-info":[{"award-number":["20254916CE340051"]}]},{"name":"China Postdoctoral Science Foundation\u2014Tianjin Joint Support Program","award":["2024T016TJ"],"award-info":[{"award-number":["2024T016TJ"]}]},{"name":"Emerging Frontiers Cultivation Program of Tianjin University Interdisciplinary Center"},{"name":"Yunnan Institute of Water and Hydropower Engineering Investigation, Design Co., Ltd."}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["J. Imaging"],"abstract":"<jats:p>Currently, standardized classification methods of surrounding rock are relatively insufficient. The classification of surrounding rock mainly relies on the subjective judgment of technicians, leading to diverse evaluation results. This study focuses on the feature extraction and classification methods of surrounding rock images in a certain tunnel of the Central Yunnan Water Diversion Project by using image processing analysis and transfer learning. Rich surrounding rock images and the water conservancy tunnel data are collected, and then the surrounding rock is classified relatively accurately according to the code and expert guidance. By introducing the fractal theory, the complexity and irregularity of the spatial distribution of weak layers and joints on the surrounding rock surface are revealed effectively. Based on the analysis of changes in fractal dimension characteristic values, a classification method for surrounding rock based on the fractal theory is proposed. Combined with the quantified parameters of surrounding rock images and the strength data collected by rebound meters, a method for correcting the surrounding rock strength based on image analysis is proposed, which can effectively solve the error caused by the uneven distribution of rock masses in the traditional rebound meter strength values. After correction, more accurate strength characteristics can be obtained, which is conducive to the standardized classification of the surrounding rock. After studying the recognition of tunnel surrounding rock images with transfer learning, a model is constructed to achieve rapid classification of tunnel surrounding rock. This research provides support for the standardized classification of tunnel surrounding rock.<\/jats:p>","DOI":"10.3390\/jimaging12020089","type":"journal-article","created":{"date-parts":[[2026,2,19]],"date-time":"2026-02-19T08:51:09Z","timestamp":1771491069000},"page":"89","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Classification of the Surrounding Rock Based on Image Processing Analysis and Transfer Learning"],"prefix":"10.3390","volume":"12","author":[{"given":"Yanyun","family":"Fan","sequence":"first","affiliation":[{"name":"Yunnan Institute of Water and Hydropower Engineering Investigation, Design Co., Ltd., Kunming 650021, China"},{"name":"Yunnan Key Laboratory of Water Security, Kunming 650021, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jiaqi","family":"Zhu","sequence":"additional","affiliation":[{"name":"Yunnan Key Laboratory of Water Security, Kunming 650021, China"},{"name":"Yunnan Institute of Water and Hydropower Engineering Investigation Design and Research, Kunming 650021, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hua","family":"Luo","sequence":"additional","affiliation":[{"name":"Yunnan Institute of Water and Hydropower Engineering Investigation, Design Co., Ltd., Kunming 650021, China"},{"name":"Yunnan Key Laboratory of Water Security, Kunming 650021, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5947-257X","authenticated-orcid":false,"given":"Yaxi","family":"Shen","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Hydraulic Engineering Simulation and Safety, Tianjin University, Tianjin 300072, China"},{"name":"School of Civil Engineering, Tianjin University, Tianjin 300072, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shuanglong","family":"Wang","sequence":"additional","affiliation":[{"name":"Yunnan Institute of Water and Hydropower Engineering Investigation, Design Co., Ltd., Kunming 650021, China"},{"name":"Yunnan Key Laboratory of Water Security, Kunming 650021, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaoning","family":"Liu","sequence":"additional","affiliation":[{"name":"Yunnan Key Laboratory of Water Security, Kunming 650021, China"},{"name":"Yunnan Institute of Water and Hydropower Engineering Investigation Design and Research, Kunming 650021, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dong","family":"Li","sequence":"additional","affiliation":[{"name":"College of Intelligence and Computing, Tianjin University, Tianjin 300072, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chuhan","family":"Deng","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Hydraulic Engineering Simulation and Safety, Tianjin University, Tianjin 300072, China"},{"name":"School of Civil Engineering, Tianjin University, Tianjin 300072, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2026,2,19]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"04016135","DOI":"10.1061\/(ASCE)GM.1943-5622.0000837","article-title":"Effect of a Fault Fracture Zone on the Stability of Tunnel-Surrounding Rock","volume":"17","author":"Wang","year":"2017","journal-title":"Int. J. Geomech."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"531","DOI":"10.1016\/S0886-7798(03)00070-1","article-title":"Systematic Numerical Simulation of Rock Tunnel Stability Considering Different Rock Conditions and Construction Effects","volume":"18","author":"Zhu","year":"2003","journal-title":"Tunn. Undergr. Sp. Technol."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"104074","DOI":"10.1016\/j.tust.2021.104074","article-title":"Analysis of the Interaction between Tunnel Support and Surrounding Rock Considering Pre-Reinforcement","volume":"115","author":"Sun","year":"2021","journal-title":"Tunn. Undergr. Sp. Technol."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Liu, B., Lin, H., and Chen, Y. (2022). Deformation Characteristics of Bolted Rock Joints under Compression-Shear Load. Appl. Sci., 12.","DOI":"10.3390\/app12105226"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1031","DOI":"10.1016\/j.conbuildmat.2018.08.011","article-title":"Comparison of Deep Convolutional Neural Networks and Edge Detectors for Image-Based Crack Detection in Concrete","volume":"186","author":"Dorafshan","year":"2018","journal-title":"Constr. Build. Mater."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"11736","DOI":"10.1016\/j.conbuildmat.2019.117367","article-title":"Image-Based Concrete Crack Detection in Tunnels Using Deep Fully Convolutional Networks","volume":"234","author":"Ren","year":"2020","journal-title":"Constr. Build. Mater."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"103291","DOI":"10.1016\/j.autcon.2020.103291","article-title":"Hybrid Pixel-Level Concrete Crack Segmentation and Quantification across Complex Backgrounds using Deep Learning","volume":"118","author":"Kang","year":"2020","journal-title":"Automat. Constr."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"120474","DOI":"10.1016\/j.conbuildmat.2020.120474","article-title":"Comparison of Crack Segmentation Using Digital Image Correlation Measurements and Deep Learning","volume":"261","author":"Amir","year":"2020","journal-title":"Constr. Build. Mater."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"133","DOI":"10.3901\/JME.2010.05.133","article-title":"Experimental Investigation on the Deformation and Crack Behavior of Rock Specimen with a Hole Undergoing Uniaxial Compressive Load","volume":"27","author":"Liu","year":"2010","journal-title":"Eng. Mech."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"9099","DOI":"10.1007\/s00603-023-03533-7","article-title":"Mechanical Deformation, Acoustic Emission Characteristics, and Microcrack Development in Porous Sandstone During the Brittle\u2013Ductile Transition","volume":"56","author":"Zhang","year":"2023","journal-title":"Rock Mech. Rock Eng."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Zhao, S., Wang, M., Yi, W., Yang, D., and Tong, J. (2022). Intelligent Classification of Surrounding Rock of Tunnel Based on 10 Machine Learning Algorithms. Appl. Sci., 12.","DOI":"10.3390\/app12052656"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"634","DOI":"10.1016\/j.media.2009.05.003","article-title":"Fractal and Multifractal Analysis: A Review","volume":"13","author":"Lopes","year":"2009","journal-title":"Med. Image Anal."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"127200","DOI":"10.1016\/j.conbuildmat.2022.127200","article-title":"Pore Structure Change and Physico-mechanical Properties Deterioration of Sandstone Suffering Freeze Thaw Actions","volume":"330","author":"Huang","year":"2022","journal-title":"Constr. Build. Mater."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"259","DOI":"10.1016\/j.ijrefrig.2022.03.002","article-title":"A Novel Defrosting Control Strategy with Image Processing Technique and Fractal Theory","volume":"138","author":"Miao","year":"2022","journal-title":"Int. J. Refrig."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"3743","DOI":"10.1080\/19648189.2022.2151517","article-title":"A Fatigue Constitutive Model for Rock Masses Based on Cross-Applications of Rheological Theory","volume":"27","author":"Zhang","year":"2023","journal-title":"Eur. J. Environ. Civ. Eng."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Pang, Y., Lin, H., Cao, P., and Meng, G. (2025). The Influence of Overlying High-Speed Rail Dynamic Loads on the Stability of Shield Tunnel Faces During Excavation. Appl. Sci., 15.","DOI":"10.3390\/app15052567"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"256","DOI":"10.1016\/j.jrmge.2019.08.001","article-title":"Evaluation of Empirical Estimation of Uniaxial Compressive Strength of Rock Using Measurements from Index and Physical Tests","volume":"12","author":"Aladejare","year":"2020","journal-title":"J. Rock Mech. Geotech."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"1927","DOI":"10.1007\/s00603-022-03155-5","article-title":"Acoustic Emissions and Seismic Tomography of Sandstone Under Uniaxial Compression: Implications for the Progressive Failure in Pillars","volume":"56","author":"Zhang","year":"2023","journal-title":"Rock Mech. Rock Eng."},{"key":"ref_19","unstructured":"Chen, C., Li, O., Barnett, A., Su, J., and Rudin, C. (2019). This Looks Like That: Deep Learning for Interpretable Image Recognition. arXiv."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Wang, J., Cao, A., Wu, Z., Liu, X., Li, Z., Lin, L., Liu, X., Li, H., and Sun, Y. (2023). Improved Surrounding Rock Classification Method for the Middle Rock Pillar of a Small Clear-Distance Tunnel. Appl. Sci., 13.","DOI":"10.3390\/app13042130"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Kovalchuk, A.V., Lebedev, A.A., Shemagina, O.V., Nuidel, I.V., Yakhno, V.G., and Stasenko, S.V. (2025). Enhancing Cascade Object Detection Accuracy Using Correctors Based on High-Dimensional Feature Separation. Technologies, 13.","DOI":"10.3390\/technologies13120593"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"2439","DOI":"10.1080\/10589759.2024.2381083","article-title":"Hybrid Machine Learning Models to Predict the Shear Strength of Discontinuities with Different Joint Wall Compressive Strength","volume":"40","author":"Xie","year":"2025","journal-title":"Nondestr. Test. Eval."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"217","DOI":"10.1561\/2000000071","article-title":"Deep Learning in Object Recognition, Detection, and Segmentation","volume":"8","author":"Wang","year":"2016","journal-title":"Found. Trends. Signal."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"104799","DOI":"10.1016\/j.cageo.2021.104799","article-title":"Deep Learning of Rock Images for Intelligent Lithology Identification","volume":"154","author":"Xu","year":"2021","journal-title":"Comput. Geosci."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Wang, H., Jiang, W., Yang, J., Xu, Z., and Zhi, B. (2025). Network Intrusion Detection Integrating Feature Dimensionality Reduction and Transfer Learning. Technologies, 13.","DOI":"10.3390\/technologies13090409"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Shao, S., Song, R., Wu, Y., Zhang, Z., Fu, H., Peng, Y., Li, Z., and Liu, Y. (2025). Research on Intelligent Predictions of Surrounding Rock Ahead of the Tunnel Face Based on Neural Network and Longitudinal Deformation Curve. Appl. Sci., 15.","DOI":"10.3390\/app15168771"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"104269","DOI":"10.1016\/j.chemolab.2021.104269","article-title":"Investigation of Transfer Learning for Image Classification and Impact on Training Sample Size","volume":"211","author":"Zhu","year":"2021","journal-title":"Chemometr. Intell. Lab."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"1796","DOI":"10.1007\/s11709-023-0002-1","article-title":"Hard-rock Tunnel Lithology Identification Using Multi-scale Dilated Convolutional Attention Network Based on Tunnel Face Images","volume":"17","author":"Zhang","year":"2024","journal-title":"Front. Struct. Civ. Eng."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"17512","DOI":"10.1038\/s41598-024-68704-0","article-title":"AI-based Rock Strength Assessment from Tunnel Face Images Using Hybrid Neural Networks","volume":"14","author":"Liu","year":"2024","journal-title":"Sci. Rep."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"114673","DOI":"10.1016\/j.jobe.2025.114673","article-title":"MARBLE-DA: Masonry Analysis with Robust, Batch-normalised, Label-free, Explainable Domain Adaptation for Crack Detection","volume":"116","author":"Fallahy","year":"2025","journal-title":"J. Build. Eng."},{"key":"ref_31","unstructured":"(2015). Standard for Engineering Classification of Rock Mass (Standard No. GB\/T 50218-2014)."}],"container-title":["Journal of Imaging"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2313-433X\/12\/2\/89\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,2,19]],"date-time":"2026-02-19T09:19:25Z","timestamp":1771492765000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2313-433X\/12\/2\/89"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,2,19]]},"references-count":31,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2026,2]]}},"alternative-id":["jimaging12020089"],"URL":"https:\/\/doi.org\/10.3390\/jimaging12020089","relation":{},"ISSN":["2313-433X"],"issn-type":[{"value":"2313-433X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,2,19]]}}}