PFVnet, A Feature Enhancement Network for Low Recognition Coal and Rock Images

PFVnet, A Feature Enhancement Network for Low Recognition Coal and Rock 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 Article PFVnet, A Feature Enhancement Network for Low Recognition Coal and Rock Images Cai Han, Zhenwen Liu, Shenglei Zhao, Yubo Li, Yanwei Duan, Xinzhou Yang, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5473818/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 29 Apr, 2025 Read the published version in Scientific Reports → Version 1 posted 6 You are reading this latest preprint version Abstract The existing coal-rock identification technology based on machine vision makes it difficult to accurately identify coal-rock images with low distinguishability. To solve this problem, a special coal-rock environment simulation experimental device was used to conduct simulations, considering various influencing factors such as illumination, air flow, coal dust, and water mist concentration. We characterized the grayscale and texture feature patterns of coal-rock media under varying degrees of interference and established a comprehensive multi-element image training sample library. The simulation experiment results show that illumination, dust, and fog can reduce the distinguishability of coal-rock images, which seriously affects the recognition performance of the network. Based on this, the convolution operation was combined with the Vision Transformer network and the deep convolution algorithm was applied to design a parallel hybrid vision network model, PFVnet. Subsequently, enhanced recognition tests were carried out in combination with the DeepLabV3 + network. The test results show that PFVnet can enhance the features of coal and rock, and achieve a PSNR of 18.90 and an SSIM of 0.58 on the multi-element image training sample library. It can effectively reduce the misjudgment of the DeepLabV3 + network, increasing its accuracy by 0.95%, the mean Intersection over Union (mIoU) by 2.15%, and the mean Pixel Accuracy (mPA) by 2.12%. This research provides new ideas and feasible technical solutions for the improvement of coal-rock identification technology and helps to promote the development of this field. Physical sciences/Energy science and technology/Fossil fuels/Coal Physical sciences/Mathematics and computing/Computer science Deep underground engineering Sustainable hazard prevention and control Coal and rock stability and failure mechanism Practice of coal pillar retention engineering Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 29 Apr, 2025 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Accepted 24 Apr, 2025 Reviews received at journal 20 Apr, 2025 Reviewers agreed at journal 20 Apr, 2025 Reviewers invited by journal 15 Apr, 2025 Submission checks completed at journal 15 Apr, 2025 First submitted to journal 10 Apr, 2025 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-5473818","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":443504441,"identity":"f0ec3a14-4f3c-4436-b0a1-5cba38aed85c","order_by":0,"name":"Cai Han","email":"","orcid":"","institution":"School of Safety Engineering, Heilongjiang University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Cai","middleName":"","lastName":"Han","suffix":""},{"id":443504442,"identity":"3357e0d9-2bd6-4824-904a-762f15e5b3d6","order_by":1,"name":"Zhenwen Liu","email":"","orcid":"","institution":"Science and Technology Department 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To solve this problem, a special coal-rock environment simulation experimental device was used to conduct simulations, considering various influencing factors such as illumination, air flow, coal dust, and water mist concentration. We characterized the grayscale and texture feature patterns of coal-rock media under varying degrees of interference and established a comprehensive multi-element image training sample library. The simulation experiment results show that illumination, dust, and fog can reduce the distinguishability of coal-rock images, which seriously affects the recognition performance of the network. Based on this, the convolution operation was combined with the Vision Transformer network and the deep convolution algorithm was applied to design a parallel hybrid vision network model, PFVnet. Subsequently, enhanced recognition tests were carried out in combination with the DeepLabV3\u0026thinsp;+\u0026thinsp;network. The test results show that PFVnet can enhance the features of coal and rock, and achieve a PSNR of 18.90 and an SSIM of 0.58 on the multi-element image training sample library. It can effectively reduce the misjudgment of the DeepLabV3\u0026thinsp;+\u0026thinsp;network, increasing its accuracy by 0.95%, the mean Intersection over Union (mIoU) by 2.15%, and the mean Pixel Accuracy (mPA) by 2.12%. 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