Features enhancement of wood defects and their recognition based on adaptive non-monotonic activation function | 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 Features enhancement of wood defects and their recognition based on adaptive non-monotonic activation function Xiao Wang, Weiguang Li, Wei Zhang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8347104/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 Deep learning based computer vision is regarded as a promising method to achieve automatic wood defects detection. However, due to the complexity and diversity of wood natural defects, the recognition accuracy is always not satisfactory to practical application. It is believed that it is the lack of specific features of wood defects that causes the lower accuracy of deep learning model. In this study, traditional activation function of CNN model is substituted by non-monotonic functions in order to achieve a better approximation ability which is though will help model to capture the key features of wood defects. The principle of Kolmogorov-Arnold Networks(KAN) is also adopted to parameterize and discretize the activation function to make it learnable and facilitate model training. Our findings indicate that some non-monotonic functions do outperform traditional activation function, ReLU. Parameterization of activation function can further improve the accuracy. By utilizing Taylor series, activation functions are discretized, and it is found that increasing the order of Taylor series will also enhance the accuracy. Specifically, new type of activation function highlights key features and can better approximate the relationships among variables, making a contribution to wood defects recognition. Moreover, by quantifying highlights of the feature maps achieved by new activation function, the specific features associated with wood structures are obtained. The findings of this study not only provide us with the guidance on improving accuracy of wood defects recognition but also make deep learning model explainable which improves its reliability. computer vision wood defects classification model deep learning activation function 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-8347104","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":563054883,"identity":"031c3197-aeb7-4b7a-b107-d1558651df29","order_by":0,"name":"Xiao 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