TIDE-YOLO: Lightweight Algorithm for Underwater Object Detection

TIDE-YOLO: Lightweight Algorithm for Underwater Object Detection | 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 TIDE-YOLO: Lightweight Algorithm for Underwater Object Detection SAMUEL ATTA ANTWI, Zhiyu Zhou This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7137519/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 This study proposes TIDE-YOLO ( T RACON + I nner-WIoU + Bi- D irectional FPN + E MPC-Detect), a lightweight algorithm designed for underwater object detection (UOD) based on the YOLOv8s framework. This algorithm addresses several challenges commonly found in underwater environments, such as blurred images, the abundance of small objects with minimal distinguishing features, and the high computational requirements of models. Firstly, the T riple Attention Mechanism (TAM) and R eceptive-Field A ttention CON volution (RFAConv) are integrated into the C2f_bottleneck to design an enhanced C2f module called TRACON. This modification enhances the receptive field of the convolutional layer, thus improving the feature extraction of the model and its ability to detect small targets. Secondly, the Bi-directional Feature Pyramid Network (BiFPN) is used to enhance the model’s contextual information capture while reducing the parameter count. Thirdly, a lightweight and efficient detection head named EMPC-Detect, which integrates EMSConv and PConv, is proposed. EMPC-Detect improves the capability of the model to capture minute object details, all the while reducing both the parameter count and computational demands of the model. Finally, Inner-WIoU loss was designed by incorporating Inner-IoU and WIoU. Inner-WIoU replaced the CIoU loss to further improve the model’s accuracy and enhanced the algorithm's ability to generalize. TIDE-YOLO was assessed using DUO, UTDAC2020, and RUOD datasets, achieving an mAP50 scores of 87.1%, 86.0%, and 86.1%, respectively. Compared to YOLOv8s, TIDE-YOLO showed a substantial decrease in model size, parameter count, and computational demands, with a reduction of 67.9%, 73.4%, and 39.3%, respectively. Underwater Target Detection RFAConv BiFPN TAM Inner-WIoU EMPC-Detect Full Text Additional Declarations No competing interests reported. Supplementary Files Supplementarymaterial.docx 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-7137519","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":498501423,"identity":"c8f0d479-55a7-4524-9aea-19b51b558647","order_by":0,"name":"SAMUEL ATTA ANTWI","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA60lEQVRIiWNgGAWjYDADNhDxAcRgJ6iUGUQagLUwzoALEKMFBJh5wCQBHfzz+w9++LnjT2If/+Gnm21+bZPnY2Zg/PAxB7cWiWPMzJK9ZwwS2yTSzG7n9t02bGNmYJacuQ2PNceYGSR42wxy2yQYgFp6bjMCtbAx8+LRIg+05edfkBb+499uW/bctieoxeAYM5s02BaGHLPbDD9uJxLUYngs2cxats24vk0ip+xmb8Pt5DZmxma8fpE7fPDxzbdtcsby/ce33fjx57bt/Pbmgx8+4vM+CmBsA5MNxKoHgT+kKB4Fo2AUjIKRAgBIcU4Zi4pMdQAAAABJRU5ErkJggg==","orcid":"","institution":"Zhejiang Sci-Tech University","correspondingAuthor":true,"prefix":"","firstName":"SAMUEL","middleName":"ATTA","lastName":"ANTWI","suffix":""},{"id":498501424,"identity":"472b6963-5e10-4335-88aa-c1fc358362d6","order_by":1,"name":"Zhiyu Zhou","email":"","orcid":"","institution":"Zhejiang Sci-Tech University","correspondingAuthor":false,"prefix":"","firstName":"Zhiyu","middleName":"","lastName":"Zhou","suffix":""}],"badges":[],"createdAt":"2025-07-16 08:23:18","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7137519/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7137519/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":89738250,"identity":"1b0fde9a-be23-44af-a616-987a325960c3","added_by":"auto","created_at":"2025-08-23 17:46:30","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":698392,"visible":true,"origin":"","legend":"","description":"","filename":"Manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7137519/v1_covered_9c275d21-da8b-4b07-b716-75620f0a20b8.pdf"},{"id":89040424,"identity":"1a5ec9c0-6970-4e9f-8e96-adf1a86ccfdc","added_by":"auto","created_at":"2025-08-14 05:34:59","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":37494516,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarymaterial.docx","url":"https://assets-eu.researchsquare.com/files/rs-7137519/v1/96da6dd2fb760171d6822aed.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"TIDE-YOLO: Lightweight Algorithm for Underwater Object Detection","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":"Underwater Target Detection, RFAConv, BiFPN, TAM, Inner-WIoU, EMPC-Detect","lastPublishedDoi":"10.21203/rs.3.rs-7137519/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7137519/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study proposes TIDE-YOLO (\u003cb\u003eT\u003c/b\u003eRACON\u0026thinsp;+\u0026thinsp;\u003cb\u003eI\u003c/b\u003enner-WIoU\u0026thinsp;+\u0026thinsp;Bi-\u003cb\u003eD\u003c/b\u003eirectional FPN\u0026thinsp;+\u0026thinsp;\u003cb\u003eE\u003c/b\u003eMPC-Detect), a lightweight algorithm designed for underwater object detection (UOD) based on the YOLOv8s framework. This algorithm addresses several challenges commonly found in underwater environments, such as blurred images, the abundance of small objects with minimal distinguishing features, and the high computational requirements of models. Firstly, the \u003cb\u003eT\u003c/b\u003eriple Attention Mechanism (TAM) and \u003cb\u003eR\u003c/b\u003eeceptive-Field \u003cb\u003eA\u003c/b\u003ettention \u003cb\u003eCON\u003c/b\u003evolution (RFAConv) are integrated into the C2f_bottleneck to design an enhanced C2f module called TRACON. This modification enhances the receptive field of the convolutional layer, thus improving the feature extraction of the model and its ability to detect small targets. Secondly, the Bi-directional Feature Pyramid Network (BiFPN) is used to enhance the model\u0026rsquo;s contextual information capture while reducing the parameter count. Thirdly, a lightweight and efficient detection head named EMPC-Detect, which integrates EMSConv and PConv, is proposed. EMPC-Detect improves the capability of the model to capture minute object details, all the while reducing both the parameter count and computational demands of the model. Finally, Inner-WIoU loss was designed by incorporating Inner-IoU and WIoU. Inner-WIoU replaced the CIoU loss to further improve the model\u0026rsquo;s accuracy and enhanced the algorithm's ability to generalize. TIDE-YOLO was assessed using DUO, UTDAC2020, and RUOD datasets, achieving an mAP50 scores of 87.1%, 86.0%, and 86.1%, respectively. Compared to YOLOv8s, TIDE-YOLO showed a substantial decrease in model size, parameter count, and computational demands, with a reduction of 67.9%, 73.4%, and 39.3%, respectively.\u003c/p\u003e","manuscriptTitle":"TIDE-YOLO: Lightweight Algorithm for Underwater Object Detection","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-08-14 05:34:52","doi":"10.21203/rs.3.rs-7137519/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":"f50bf47e-7258-4642-a9dc-89ea71619a44","owner":[],"postedDate":"August 14th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-08-23T17:38:21+00:00","versionOfRecord":[],"versionCreatedAt":"2025-08-14 05:34:52","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7137519","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7137519","identity":"rs-7137519","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}