A-RepVGG: Research on Classification Algorithms based on Deep Learning and Wood CT Images

A-RepVGG: Research on Classification Algorithms based on Deep Learning and Wood CT Images | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article A-RepVGG: Research on Classification Algorithms based on Deep Learning and Wood CT Images Zhishuai Zheng, Zhedong Ge, Xiaoxia Yang, Xiaotong Liu, Lipeng Qin, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4021077/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract To address the issue of limited expressive ability and performance degradation of the model caused by the limited depth and width of the CNN because of low computational overhead of lightweight convolutional neural networks, this paper introduces a classification method called A-RepVGG for wood CT images. A-RepVGG aims to enhance the model's classification accuracy in terms of wood microstructure by increasing the model complexity without increasing the depth and width of the network. The method utilizes adaptive convolution to dynamically aggregate convolution kernels based on the input image. This allows the convolution kernels to have optimal receptive fields on different layers of features, enabling a more comprehensive extraction of information, such as wood texture, tubular pore distribution, and cell arrangement. Additionally, a multi-scale null attention mechanism is incorporated into the network, multi-layer convolution is employed to extract feature maps of different scales and weighted fusion is adopted to emphasize important feature regions. This effectively captures both local and global information of the image. Finally, the ELU activation function is introduced to ensure that the feature information can be properly output in the negative half-axis, thereby facilitating a more thorough extraction of wood feature information and improving the model's classification accuracy. The study aimed to classify 20 species of wood cross-section microscopic images. The findings revealed that the A-RepVGG model outperformed other existing wood image classification models, such as ResNet, ResNeSt, and ViT. The A-RepVGG model achieved an impressive accuracy of 99.50% on the test set and 99.20% on the validation set. This model incorporates adaptive convolution and attention mechanisms, which effectively enhance the classification accuracy of wood microscopic images. These results highlight the potential of deep learning in automatic classification of wood species and provide valuable insights for micro-level wood classification. CT Image RepVGG MSDA ACM Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4021077","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":277997579,"identity":"8263a9b5-bb63-49ed-a416-173fff48333d","order_by":0,"name":"Zhishuai Zheng","email":"","orcid":"","institution":"Shandong Jianzhu University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Zhishuai","middleName":"","lastName":"Zheng","suffix":""},{"id":277997581,"identity":"257390fb-cc71-473b-9c26-7ef1d0ea1f85","order_by":1,"name":"Zhedong 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A-RepVGG aims to enhance the model's classification accuracy in terms of wood microstructure by increasing the model complexity without increasing the depth and width of the network. The method utilizes adaptive convolution to dynamically aggregate convolution kernels based on the input image. This allows the convolution kernels to have optimal receptive fields on different layers of features, enabling a more comprehensive extraction of information, such as wood texture, tubular pore distribution, and cell arrangement. Additionally, a multi-scale null attention mechanism is incorporated into the network, multi-layer convolution is employed to extract feature maps of different scales and weighted fusion is adopted to emphasize important feature regions. This effectively captures both local and global information of the image. 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