TLDRT-DETR: Adaptive Upsampling and Dual-Activation Attention for Real-Time Transmission Line Defect Detection

TLDRT-DETR: Adaptive Upsampling and Dual-Activation Attention for Real-Time Transmission Line Defect 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 TLDRT-DETR: Adaptive Upsampling and Dual-Activation Attention for Real-Time Transmission Line Defect Detection Bing Su, Yi Lu, Yifeng Lin This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8599290/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 15 You are reading this latest preprint version Abstract Defects in transmission line components pose serious threats to the safety and reliability of power systems. However, accurate detection remains challenging due to large scale variations, complex outdoor backgrounds, and the coexistence of subtle and prominent defect patterns. To address these challenges, we propose TLDRT-DETR, an improved real-time detection framework based on RT-DETR. Specifically, we design an Adaptive Attention Dynamic Upsampling (AADU) module to replace conventional upsampling in cross-scale feature fusion, enabling content-adaptive feature reconstruction and better preservation of multi-scale structural information. In addition, a Dual-activation Spatial and Channel Synergistic Attention (DualActSCSA) module is introduced into high-level feature fusion to enhance defect feature discriminability and suppress background interference. Experimental results show that the proposed method achieves 90.5% Precision, 85.4% mAP@50, and 55.7% mAP@50:95, outperforming the RT-DETR baseline by 2.2%, 1.7%, and 1.8%, respectively. These results demonstrate that TLDRT-DETR provides more accurate and robust defect detection in complex transmission line inspection environments. transmission line detection multi-scale defect detection real-time object detection Transformer-based detector adaptive upsampling attention-guided feature fusion Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 18 Apr, 2026 Reviews received at journal 15 Apr, 2026 Reviewers agreed at journal 25 Mar, 2026 Reviews received at journal 25 Mar, 2026 Reviews received at journal 22 Mar, 2026 Reviewers agreed at journal 22 Mar, 2026 Reviewers agreed at journal 22 Mar, 2026 Reviewers agreed at journal 21 Mar, 2026 Reviewers agreed at journal 21 Mar, 2026 Reviews received at journal 21 Mar, 2026 Reviewers agreed at journal 21 Mar, 2026 Reviewers invited by journal 20 Mar, 2026 Editor assigned by journal 20 Mar, 2026 Submission checks completed at journal 16 Jan, 2026 First submitted to journal 14 Jan, 2026 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-8599290","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":611602352,"identity":"95cd9ec8-f6df-4dbb-9480-1cb39574cf1b","order_by":0,"name":"Bing Su","email":"","orcid":"","institution":"Changzhou University","correspondingAuthor":false,"prefix":"","firstName":"Bing","middleName":"","lastName":"Su","suffix":""},{"id":611602353,"identity":"86b8407b-64e9-4e63-954f-4a6526351ec2","order_by":1,"name":"Yi Lu","email":"","orcid":"","institution":"Changzhou University","correspondingAuthor":false,"prefix":"","firstName":"Yi","middleName":"","lastName":"Lu","suffix":""},{"id":611602354,"identity":"a6b38a3a-6a14-420c-acc6-34cc2ebb9a13","order_by":2,"name":"Yifeng Lin","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABDElEQVRIie2QwUoDMRCGZ4nES9q9phf3FbIsiGApPsqEwnoTQdhzTjkVvC546CvYN0gJeFrWq1LB9Q3qbT0UTZVWhGb1WDAf5GcmzAfDAAQCe0ifuMDhtt9U1K/QTyXf9puqS/kxCGD/oBz27niDw4vkjLy8vun7JL6uBCwLC/GN8izWzzlifpUamvGBXqSlqURU1hb4k/Eo7JjL1sqZYhRSvUARTQTpaQuCo19BfP9SpK5RECbI6nfFyCk4Za4NCuqUqFvJThDH8hZoNlD1OC0rejmf1OeMP+xW4rhKH1scyalyF2uLkbuYnTVtcXoUl7sVxwFfp3huvr+Me8w37yDLdSaqYyQQCAT+Nx9S31KR4XkNLAAAAABJRU5ErkJggg==","orcid":"","institution":"Changzhou University","correspondingAuthor":true,"prefix":"","firstName":"Yifeng","middleName":"","lastName":"Lin","suffix":""}],"badges":[],"createdAt":"2026-01-14 08:23:36","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8599290/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8599290/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":105565553,"identity":"e1208fa5-2298-4fea-8c74-dd0fcf70c36a","added_by":"auto","created_at":"2026-03-27 12:53:35","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":62195772,"visible":true,"origin":"","legend":"","description":"","filename":"TLDRTDETR.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8599290/v1_covered_d4bcea16-88cf-46b7-859d-8ab5bbd0b9e3.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"TLDRT-DETR: Adaptive Upsampling and Dual-Activation Attention for Real-Time Transmission Line Defect Detection","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"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":"the-journal-of-supercomputing","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [The Journal of Supercomputing](https://www.springer.com/journal/11227)","snPcode":"11227","submissionUrl":"https://submission.nature.com/new-submission/11227/3","title":"The Journal of Supercomputing","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"transmission line detection, multi-scale defect detection, real-time object detection, Transformer-based detector, adaptive upsampling, attention-guided feature fusion","lastPublishedDoi":"10.21203/rs.3.rs-8599290/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8599290/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eDefects in transmission line components pose serious threats to the safety and reliability of power systems. However, accurate detection remains challenging due to large scale variations, complex outdoor backgrounds, and the coexistence of subtle and prominent defect patterns. To address these challenges, we propose TLDRT-DETR, an improved real-time detection framework based on RT-DETR. Specifically, we design an Adaptive Attention Dynamic Upsampling (AADU) module to replace conventional upsampling in cross-scale feature fusion, enabling content-adaptive feature reconstruction and better preservation of multi-scale structural information. In addition, a Dual-activation Spatial and Channel Synergistic Attention (DualActSCSA) module is introduced into high-level feature fusion to enhance defect feature discriminability and suppress background interference. Experimental results show that the proposed method achieves 90.5% Precision, 85.4% mAP@50, and 55.7% mAP@50:95, outperforming the RT-DETR baseline by 2.2%, 1.7%, and 1.8%, respectively. These results demonstrate that TLDRT-DETR provides more accurate and robust defect detection in complex transmission line inspection environments.\u003c/p\u003e","manuscriptTitle":"TLDRT-DETR: Adaptive Upsampling and Dual-Activation Attention for Real-Time Transmission Line Defect Detection","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-25 10:27:37","doi":"10.21203/rs.3.rs-8599290/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-04-18T20:53:20+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-15T06:58:41+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"161797388178947947984753406753446364380","date":"2026-03-26T02:40:32+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-25T08:08:34+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-23T02:18:39+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"298559261131921464087487085853720233120","date":"2026-03-23T02:00:16+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"8854344261389745278810651007690802584","date":"2026-03-22T06:26:10+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"229045083036403689486239981076493998205","date":"2026-03-21T09:17:59+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"289990647402576691444109868851019818920","date":"2026-03-21T09:05:31+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-21T08:59:50+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"336830137101069324875802917832287594696","date":"2026-03-21T04:45:14+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-03-20T16:53:25+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-03-20T16:40:23+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-01-16T05:45:03+00:00","index":"","fulltext":""},{"type":"submitted","content":"The Journal of Supercomputing","date":"2026-01-14T08:18:29+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"the-journal-of-supercomputing","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [The Journal of Supercomputing](https://www.springer.com/journal/11227)","snPcode":"11227","submissionUrl":"https://submission.nature.com/new-submission/11227/3","title":"The Journal of Supercomputing","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"cb22d07d-518c-4e45-ac31-93b0bc7ebc1e","owner":[],"postedDate":"March 25th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-05-14T03:23:16+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-25 10:27:37","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8599290","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8599290","identity":"rs-8599290","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}