ANX-Net: A Fast and Resource Optimized Network for Image Dehazing for Driving in Haze Weather Conditions

ANX-Net: A Fast and Resource Optimized Network for Image Dehazing for Driving in Haze Weather Conditions | 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 ANX-Net: A Fast and Resource Optimized Network for Image Dehazing for Driving in Haze Weather Conditions Yun Zhu, Shaoshan Niu, Guo Jia, Yan Su This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5320316/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 Severe weather conditions such as haze will bring serious problems to the safe driving of autonomous vehicle. In order to ensure that autonomous vehicle can still run safely in frequent bad weather, the research of image dehazing algorithm is very important. The key to safe and reliable driving is that autonomous vehicle can obtain clear images in severe haze weather conditions. Therefore, ensuring the dehazing performance of the dehazing algorithm is very important. In this paper, we propose ANX-Net, which is a robust and reliable dehazing network for autonomous vehicle. The network uses components such as feature extraction module, channel attention module, multi-scale spatial attention module and gsconv module to effectively dehaze the images taken by the autonomous vehicle camera. Through a detailed qualitative and quantitative evaluation of the road traffic dataset AAR in hazy weather, the effectiveness of the proposed network was analyzed, demonstrating its good dehazing performance. Adverse weather Deep learning Channel attention module Multi-scale spatial attention module GSConv module 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-5320316","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":369947568,"identity":"dee6ddaa-0c8b-4de3-808b-134bc0cd2eb0","order_by":0,"name":"Yun Zhu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA20lEQVRIiWNgGAWjYDACCTBpw8DYAGYwE60lDaQFpIt4LYdBBJFa5Gf3mEnz7jifxzwj/fkDhgrrxAb2swfwamGccwao5cztYsYZOYYNDGfSExt48hLwamGWyAFqabud2Dgjh7GBse1wYoMEjwFeLWwQLeeAWtIfNjD+I0ILD0TLAaCWBMMGxgYitEhIpBVbzm1LTmzseWM4I+FYunEbTw5+LfIzkjfeeNtml7ixPf3Bhw811rL97GfwawECFnDUAIOLgSEB5DtC6oGA+QPYOiJUjoJRMApGwQgFAE2IQzuAtIA5AAAAAElFTkSuQmCC","orcid":"","institution":"Nanjing University of Science and Technology","correspondingAuthor":true,"prefix":"","firstName":"Yun","middleName":"","lastName":"Zhu","suffix":""},{"id":369947569,"identity":"25a4d637-425f-46aa-95bb-7278cd1f5272","order_by":1,"name":"Shaoshan Niu","email":"","orcid":"","institution":"Nanjing University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Shaoshan","middleName":"","lastName":"Niu","suffix":""},{"id":369947571,"identity":"0fbe1d5c-fe5e-42ac-88d0-5c86a04cf72f","order_by":2,"name":"Guo Jia","email":"","orcid":"","institution":"Nanjing University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Guo","middleName":"","lastName":"Jia","suffix":""},{"id":369947573,"identity":"9400fd0d-74e0-4d6a-8d9e-36516f30bf4e","order_by":3,"name":"Yan Su","email":"","orcid":"","institution":"Nanjing University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Yan","middleName":"","lastName":"Su","suffix":""}],"badges":[],"createdAt":"2024-10-23 15:53:16","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5320316/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5320316/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":70627302,"identity":"73360418-5059-4b4f-b865-d0e68dadb599","added_by":"auto","created_at":"2024-12-05 04:54:35","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":726826,"visible":true,"origin":"","legend":"","description":"","filename":"paper.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5320316/v1_covered_38971066-a0a9-4ff3-ac6e-9a5e39ed1af8.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"ANX-Net: A Fast and Resource Optimized Network for Image Dehazing for Driving in Haze Weather Conditions","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Adverse weather, Deep learning, Channel attention module, Multi-scale spatial attention module, GSConv module","lastPublishedDoi":"10.21203/rs.3.rs-5320316/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5320316/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e \u003ca class=\"FNLink\" href=\"#Fn1\" id=\"#FNLinkFn1\"\u003e\u003c/a\u003e Severe weather conditions such as haze will bring serious problems to the safe driving of autonomous vehicle. In order to ensure that autonomous vehicle can still run safely in frequent bad weather, the research of image dehazing algorithm is very important. The key to safe and reliable driving is that autonomous vehicle can obtain clear images in severe haze weather conditions. Therefore, ensuring the dehazing performance of the dehazing algorithm is very important. In this paper, we propose ANX-Net, which is a robust and reliable dehazing network for autonomous vehicle. The network uses components such as feature extraction module, channel attention module, multi-scale spatial attention module and gsconv module to effectively dehaze the images taken by the autonomous vehicle camera. Through a detailed qualitative and quantitative evaluation of the road traffic dataset AAR in hazy weather, the effectiveness of the proposed network was analyzed, demonstrating its good dehazing performance.\u003c/p\u003e","manuscriptTitle":"ANX-Net: A Fast and Resource Optimized Network for Image Dehazing for Driving in Haze Weather Conditions","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-10-28 11:58:14","doi":"10.21203/rs.3.rs-5320316/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"ec68cc5c-bbc5-4b43-a300-0271231beaac","owner":[],"postedDate":"October 28th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-12-05T04:54:15+00:00","versionOfRecord":[],"versionCreatedAt":"2024-10-28 11:58:14","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5320316","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5320316","identity":"rs-5320316","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}