WBi-YOLOSF: Improved FPN for aquatic real-time target detection under the Artificial rabbits optimization

WBi-YOLOSF: Improved FPN for aquatic real-time target detection under the Artificial rabbits optimization | 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 WBi-YOLOSF: Improved FPN for aquatic real-time target detection under the Artificial rabbits optimization Liubing Jiang, Yujie Mu, Li Che, Yongman Wu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4331920/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 03 Aug, 2024 Read the published version in Scientific Reports → Version 1 posted 10 You are reading this latest preprint version Abstract As the pillar industry of coastal areas, aquaculture needs artificial intelligence technology to promote economic development. To realize the automation of the aquaculture industry, this paper proposes a new underwater object detection network: WBi-YOLOSF. It realizes the automatic classification and detection of aquatic products, improves the production efficiency of the aquaculture industry, and promotes its economic development. This paper creates an image dataset containing 15 aquatic products to lay the data foundation for model training. In the data preprocessing part, an underwater image enhancement algorithm is proposed to improve the quality of the data set effectively. Aiming at the problem of high false detection rate and missed detection rate of underwater dense small targets, a new data enhancement method was proposed to improve the training set's data quality comprehensively. Inspired by the weighted bidirectional feature pyramid network, this paper proposes a new feature extraction network: AU-BiFPN, which solves the gradient problem caused by the network hierarchy's deepening on enhancing the network's multi-scale feature fusion. Here, the swarm intelligence algorithm is introduced to optimize the model hyperparameters, accelerating the convergence speed of model training and significantly reducing the computational cost. At the same time, the model's accuracy is improved by a cliff. In addition, the FReLU activation function is introduced in the network's backbone, and the SimAM attention mechanism is integrated, effectively improving the accuracy and speed of the model prediction. Ablation and comparison experiments show the effectiveness and superiority of the proposed model. Verified by the mAP and FPS evaluation indicators, the experimental results of the WBi-YOLOSF target detection network can reach 0.982 and 203 frames per second, which are 1.4% and five frames per second higher than the network with the second score. In summary, this method can quickly and accurately identify aquatic products, realize real-time target detection of aquatic products, and lay the foundation for developing an aquaculture automation system. Physical sciences/Mathematics and computing/Computer science Physical sciences/Mathematics and computing/Information technology Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 03 Aug, 2024 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 07 Jun, 2024 Reviews received at journal 06 Jun, 2024 Reviewers agreed at journal 30 May, 2024 Reviews received at journal 19 May, 2024 Reviewers agreed at journal 05 May, 2024 Reviewers invited by journal 30 Apr, 2024 Editor assigned by journal 30 Apr, 2024 Editor invited by journal 30 Apr, 2024 Submission checks completed at journal 29 Apr, 2024 First submitted to journal 26 Apr, 2024 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. 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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-4331920","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":298955613,"identity":"1c6e027e-6c03-4de7-bc13-74fab226328f","order_by":0,"name":"Liubing Jiang","email":"","orcid":"","institution":"Guilin University of Electronic Technology","correspondingAuthor":false,"prefix":"","firstName":"Liubing","middleName":"","lastName":"Jiang","suffix":""},{"id":298955616,"identity":"04ce5154-b667-4eed-b258-4d374930f8ef","order_by":1,"name":"Yujie Mu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA00lEQVRIiWNgGAWjYLACCQMGOTb2xsYHCRU1xGsx5uc53Gzw4Mwx4i1KnDkjvU3yYQszYaW6M3LMJCwKbBg3HEhsq0hsYGPgb+9OwKvF7AZQi4RBGrPBgYNtNxJ3yDBInDm7gRgth9kMDjYCtZxhYzCQyCVKy38eg8OMbQWJbcxEazkgIdnG2MZAnJYzz4otJAySDfh5GJslEs4c4yHsl+PJG29L/LGrb5N//vDjj4oaOf72XvxaGBg4DJglkLg8BJSDAPsDxg9EKBsFo2AUjIIRDABq6EmGlUvLUwAAAABJRU5ErkJggg==","orcid":"","institution":"Guilin University of Electronic Technology","correspondingAuthor":true,"prefix":"","firstName":"Yujie","middleName":"","lastName":"Mu","suffix":""},{"id":298955619,"identity":"3e12e4f3-e9a5-46b0-ab4e-bff566ba5f6d","order_by":2,"name":"Li Che","email":"","orcid":"","institution":"Guilin University of Electronic Technology","correspondingAuthor":false,"prefix":"","firstName":"Li","middleName":"","lastName":"Che","suffix":""},{"id":298955622,"identity":"c4cb2d67-5197-4502-844d-f89a5b974295","order_by":3,"name":"Yongman Wu","email":"","orcid":"","institution":"Guilin University of Electronic Technology","correspondingAuthor":false,"prefix":"","firstName":"Yongman","middleName":"","lastName":"Wu","suffix":""}],"badges":[],"createdAt":"2024-04-27 01:25:33","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4331920/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4331920/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-024-68878-7","type":"published","date":"2024-08-03T15:58:16+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":61793850,"identity":"8591cb3a-9821-4937-9dd4-6f33775cd9c8","added_by":"auto","created_at":"2024-08-05 16:16:06","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4017253,"visible":true,"origin":"","legend":"","description":"","filename":"WBiYOLOSFImprovedFPNforaquaticrealtimetargetdetectionundertheArtificialrabbitsoptimization2.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4331920/v1_covered_060d92d2-37eb-4378-92ce-6acbb736b2d7.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"WBi-YOLOSF: Improved FPN for aquatic real-time target detection under the Artificial rabbits optimization","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-4331920/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4331920/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"As the pillar industry of coastal areas, aquaculture needs artificial intelligence technology to promote economic development. To realize the automation of the aquaculture industry, this paper proposes a new underwater object detection network: WBi-YOLOSF. It realizes the automatic classification and detection of aquatic products, improves the production efficiency of the aquaculture industry, and promotes its economic development. This paper creates an image dataset containing 15 aquatic products to lay the data foundation for model training. In the data preprocessing part, an underwater image enhancement algorithm is proposed to improve the quality of the data set effectively. Aiming at the problem of high false detection rate and missed detection rate of underwater dense small targets, a new data enhancement method was proposed to improve the training set's data quality comprehensively. Inspired by the weighted bidirectional feature pyramid network, this paper proposes a new feature extraction network: AU-BiFPN, which solves the gradient problem caused by the network hierarchy's deepening on enhancing the network's multi-scale feature fusion. Here, the swarm intelligence algorithm is introduced to optimize the model hyperparameters, accelerating the convergence speed of model training and significantly reducing the computational cost. At the same time, the model's accuracy is improved by a cliff. In addition, the FReLU activation function is introduced in the network's backbone, and the SimAM attention mechanism is integrated, effectively improving the accuracy and speed of the model prediction. Ablation and comparison experiments show the effectiveness and superiority of the proposed model. 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