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App. Res. of Comp. Accepted Paper
Vol. 43, 2026 No. 1

Quantitative evaluation model for surface roughness of stone cultural relics based on multi-scale convolution and local-to-global transformer

Chen Yaxin1a
Zhou Qiang1a
Liu Xin1a
Wang Ying1b
Zhu Jianfeng1b
Luo Hongjie2
1. a. School of Electrical & Control Engineering, b. School of Materials Science & Engineering, Shaanxi University of Science & Technology, Xi'an 710021, China
2. Key Laboratory of Silicate Cultural Relics Conservation(Ministry of Education), Shanghai University, Shanghai 200444, China
DOI: 10.19734/j.issn.1001-3695.2025.06.0202

Abstract

Surface roughness measurement is crucial for the conservation and research of stone cultural relics. Existing methods for quantitative evaluation of rock profile roughness rely on manually designed features, which often fail to comprehensively capture detailed surface characteristics. This study developed a deep learning-based model, which integrates multi-scale convolution and a local-to-global Transformer for quantitative assessment of surface roughness(HTCBNet) . First, the Distribution-Focused Feature Extraction module normalizes profile lines in data distribution space while preserving their amplitude characteristics. Subsequently, the Heterogeneous Fusion Feature Extraction module is proposed, where subnetworks employ Multi-scale Convolution architecture diagram and LGFormer to extract local and global features. These features are then fused using an adaptive feature fusion module, supplemented by the Bi-GRU model to capture sequential profile line features, thereby enhancing the model’s feature extraction capability. Finally, the JRC Prediction Head is developed to map the extracted profile features into joint roughness coefficient (JRC) . Experiments demonstrate that HTCBNet achieves a goodness-of-fit (R2) of 0.99 and a mean prediction error of 0.02, outperforming traditional feature-based methods in both accuracy and generalization ability.

Foundation Support

陕西省科技厅工业项目(2024GX-YBXM-544)
上海大学硅酸盐质文物保护教育部重点实验室开放课题(SCRC2024KF04TS)
陕西省地下文物保护利用协同创新中心项目(22JY008)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2025.06.0202
Publish at: Application Research of Computers Accepted Paper, Vol. 43, 2026 No. 1

Publish History

[2025-09-17] Accepted Paper

Cite This Article

陈雅鑫, 周强, 刘鑫, 等. 基于多尺度卷积和局部到全局Transformer的石质文物表面粗糙度定量评价模型 [J]. 计算机应用研究, 2026, 43 (1). (2025-09-17). https://doi.org/10.19734/j.issn.1001-3695.2025.06.0202. (Chen Yaxin, Zhou Qiang, Liu Xin, et al. Quantitative evaluation model for surface roughness of stone cultural relics based on multi-scale convolution and local-to-global transformer [J]. Application Research of Computers, 2026, 43 (1). (2025-09-17). https://doi.org/10.19734/j.issn.1001-3695.2025.06.0202. )

About the Journal

  • Application Research of Computers Monthly Journal
  • Journal ID ISSN 1001-3695
    CN  51-1196/TP

Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

Aiming at the urgently needed cutting-edge technology in this discipline, Application Research of Computers reflects the mainstream technology, hot technology and the latest development trend of computer application research at home and abroad in a timely manner. The main contents of the journal include high-level academic papers in this discipline, the latest scientific research results and major application results. The contents of the columns involve new theories of computer discipline, basic computer theory, algorithm theory research, algorithm design and analysis, blockchain technology, system software and software engineering technology, pattern recognition and artificial intelligence, architecture, advanced computing, parallel processing, database technology, computer network and communication technology, information security technology, computer image graphics and its latest hot application technology.

Application Research of Computers has many high-level readers and authors, and its readers are mainly senior and middle-level researchers and engineers engaged in the field of computer science, as well as teachers and students majoring in computer science and related majors in colleges and universities. Over the years, the total citation frequency and Web download rate of Application Research of Computers have been ranked among the top of similar academic journals in this discipline, and the academic papers published are highly popular among the readers for their novelty, academics, foresight, orientation and practicality.

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