HSFL-YOLO: Improved YOLOv12-based target detection of wheat aphids in the field

HSFL-YOLO: Improved YOLOv12-based target detection of wheat aphids in the field | 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 HSFL-YOLO: Improved YOLOv12-based target detection of wheat aphids in the field Haiyan Lü, Shujie Zhang, Hongbo Qiao, Jianping Wang, Jianjun Zhang, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9256723/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 Wheat aphids are major pests threatening wheat production, and accurate field detection is essential for pest management and food security. However, in complex field environments, aphid detection remains challenging due to the small size of aphids, weak visual features, and severe background interference, which often result in missed detections and false alarms in traditional methods. To address these issues, this study proposes a YOLO (You Only Look Once)-based small object detection method called HSFL-YOLO. First, a HAFB (Hierarchical Attention Fusion Block) feature fusion module is designed to enhance information exchange among multi-scale features and improve the fusion of shallow detail features with deep semantic features. Second, the original PAFPN (Path Aggregation Feature Pyramid Network) is optimized by constructing a SOEP (Small Object Enhancement Pyramid) structure, which retains more positional information for small targets and enhances the model’s perception of small objects. In addition, the FRFN (Feature Refinement Feed-Forward Network) module is introduced to adaptively reconstruct features and strengthen channel-wise interaction, thereby improving feature representation in dense small-target regions. Finally, the GCD(Gaussian Combined Distance) loss and Focal-EIoU(Focal Efficient Intersection over Union)loss are combined to enhance the model’s focus on hard samples and improve bounding box regression accuracy. Experimental results on the wheat aphid dataset show that, compared with the baseline model, HSFL-YOLO improves precision, recall, mAP(mean Average Precision)@0.5, and [email protected] :0.95 by 2.3%, 1.5%, 0.4%, and 1.1%, respectively. The proposed method effectively improves small object detection performance while maintaining reasonable model complexity, providing technical support for intelligent pest detection in complex agricultural scenarios. Wheat aphids Small object detection YOLO Multi-scale feature fusion Intelligent pest detection 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-9256723","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":616244080,"identity":"f933a84f-9c42-4988-99b9-e6744fcc0247","order_by":0,"name":"Haiyan Lü","email":"","orcid":"","institution":"Henan Agricultural University","correspondingAuthor":false,"prefix":"","firstName":"Haiyan","middleName":"","lastName":"Lü","suffix":""},{"id":616244081,"identity":"2a571d6c-391b-4cd8-8a0d-b221236d4f07","order_by":1,"name":"Shujie Zhang","email":"","orcid":"","institution":"Henan Agricultural 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However, in complex field environments, aphid detection remains challenging due to the small size of aphids, weak visual features, and severe background interference, which often result in missed detections and false alarms in traditional methods. To address these issues, this study proposes a YOLO (You Only Look Once)-based small object detection method called HSFL-YOLO. First, a HAFB (Hierarchical Attention Fusion Block) feature fusion module is designed to enhance information exchange among multi-scale features and improve the fusion of shallow detail features with deep semantic features. Second, the original PAFPN (Path Aggregation Feature Pyramid Network) is optimized by constructing a SOEP (Small Object Enhancement Pyramid) structure, which retains more positional information for small targets and enhances the model\u0026rsquo;s perception of small objects. In addition, the FRFN (Feature Refinement Feed-Forward Network) module is introduced to adaptively reconstruct features and strengthen channel-wise interaction, thereby improving feature representation in dense small-target regions. Finally, the GCD(Gaussian Combined Distance) loss and Focal-EIoU(Focal Efficient Intersection over Union)loss are combined to enhance the model\u0026rsquo;s focus on hard samples and improve bounding box regression accuracy. Experimental results on the wheat aphid dataset show that, compared with the baseline model, HSFL-YOLO improves precision, recall, mAP(mean Average Precision)@0.5, and [email protected]:0.95 by 2.3%, 1.5%, 0.4%, and 1.1%, respectively. The proposed method effectively improves small object detection performance while maintaining reasonable model complexity, providing technical support for intelligent pest detection in complex agricultural scenarios.\u003c/p\u003e","manuscriptTitle":"HSFL-YOLO: Improved YOLOv12-based target detection of wheat aphids in the field","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-07 19:14:49","doi":"10.21203/rs.3.rs-9256723/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":"ff1e54ac-71ac-4096-9175-97a18bcbeeca","owner":[],"postedDate":"April 7th, 2026","published":true,"recentEditorialEvents":[{"type":"editorInvitedReview","content":"","date":"2026-05-01T12:47:07+00:00","index":13,"fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-04-07T19:14:49+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-07 19:14:49","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9256723","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9256723","identity":"rs-9256723","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}