Collaborative Detection of Power Equipment Failures and Worker Protection for Hydropower Stations: Research on the Lightweight DRR-YOLOv11s Model

Collaborative Detection of Power Equipment Failures and Worker Protection for Hydropower Stations: Research on the Lightweight DRR-YOLOv11s Model | 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 Collaborative Detection of Power Equipment Failures and Worker Protection for Hydropower Stations: Research on the Lightweight DRR-YOLOv11s Model Ping Yu, Baoshu Zong, Xiaozhong Geng, Shuaiting Chen, Yong Li This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7944519/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 24 Apr, 2026 Read the published version in Scientific Reports → Version 1 posted 11 You are reading this latest preprint version Abstract The growing demand for intelligent operation and maintenance of hydropower plants highlights the limitations of traditional detection methods. These methods cannot fully satisfy the demand for real-time capability, efficiency, and accuracy in equipment fault identification and ensuring worker safety. To resolve this problem, we present DDR-YOLOv11s, a lightweight detection algorithm based on YOLOv11s. The model integrates three efficient modules: DDSConv, which replaces standard convolutions to reduce the amount of parameters and FLOPs; RDSC, an improved detection head optimized for small-object detection; and enhanced RepViTBlocks, which substitute C3K2/C3K bottlenecks to strengthen feature representation and information exchange. DDR-YOLOv11s demonstrates significant lightweight performance. Relative to the baseline model, the parameter count decreased by 37.58% and the computational cost was lowered by 28.64%, yielding a total of 5.88M parameters and 15.2G FLOPs. On the Electrical Equipment Failure dataset (Roboflow), it achieves a precision of 92.50%, a recall of 89.42%, an [email protected] of 93.46%, and an [email protected] :0.95 of 73.45%, with an inference speed of 160.22 FPS. Cross-dataset evaluation on a PPE dataset confirms its strong generalization ability. These results indicate that DDR-YOLOv11s attains a favorable balance between accuracy and computational cost, highlighting its applicability for edge deployment in hydropower monitoring systems. Physical sciences/Energy science and technology Physical sciences/Engineering Physical sciences/Mathematics and computing lightweight modeling hydroelectric plant power equipment failure personal protective equipment Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 24 Apr, 2026 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 17 Nov, 2025 Reviews received at journal 14 Nov, 2025 Reviews received at journal 09 Nov, 2025 Reviewers agreed at journal 05 Nov, 2025 Reviewers agreed at journal 03 Nov, 2025 Reviewers agreed at journal 31 Oct, 2025 Reviewers invited by journal 31 Oct, 2025 Editor invited by journal 29 Oct, 2025 Editor assigned by journal 25 Oct, 2025 Submission checks completed at journal 25 Oct, 2025 First submitted to journal 25 Oct, 2025 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-7944519","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":542544434,"identity":"12ad471f-c934-4f3c-b583-e86de8e867e7","order_by":0,"name":"Ping Yu","email":"","orcid":"","institution":"Changchun Institute of Technology","correspondingAuthor":false,"prefix":"","firstName":"Ping","middleName":"","lastName":"Yu","suffix":""},{"id":542544435,"identity":"f79be5e8-9094-46b2-9aca-a21df50737dd","order_by":1,"name":"Baoshu Zong","email":"","orcid":"","institution":"Changchun Institute of 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hydroelectric plant, power equipment failure, personal protective equipment","lastPublishedDoi":"10.21203/rs.3.rs-7944519/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7944519/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe growing demand for intelligent operation and maintenance of hydropower plants highlights the limitations of traditional detection methods. These methods cannot fully satisfy the demand for real-time capability, efficiency, and accuracy in equipment fault identification and ensuring worker safety. To resolve this problem, we present DDR-YOLOv11s, a lightweight detection algorithm based on YOLOv11s. The model integrates three efficient modules: DDSConv, which replaces standard convolutions to reduce the amount of parameters and FLOPs; RDSC, an improved detection head optimized for small-object detection; and enhanced RepViTBlocks, which substitute C3K2/C3K bottlenecks to strengthen feature representation and information exchange. DDR-YOLOv11s demonstrates significant lightweight performance. Relative to the baseline model, the parameter count decreased by 37.58% and the computational cost was lowered by 28.64%, yielding a total of 5.88M parameters and 15.2G FLOPs. 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These results indicate that DDR-YOLOv11s attains a favorable balance between accuracy and computational cost, highlighting its applicability for edge deployment in hydropower monitoring systems.\u003c/p\u003e","manuscriptTitle":"Collaborative Detection of Power Equipment Failures and Worker Protection for Hydropower Stations: Research on the Lightweight DRR-YOLOv11s Model","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-11 08:51:50","doi":"10.21203/rs.3.rs-7944519/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-11-17T07:27:50+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-11-14T16:17:34+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-11-09T15:19:13+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"45137466517180901416629727865309690415","date":"2025-11-05T09:25:53+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"182569168113971217677210967511469815932","date":"2025-11-04T04:34:44+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"174650210036762661422328652494584475995","date":"2025-10-31T09:25:04+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-10-31T09:19:56+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-10-29T07:41:15+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-10-25T10:24:19+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-10-25T10:23:17+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2025-10-25T05:21:27+00:00","index":"","fulltext":""}],"status":"published","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}}],"origin":"","ownerIdentity":"277d6825-a633-49d5-ad46-3b7c95426c61","owner":[],"postedDate":"November 11th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":57714023,"name":"Physical sciences/Energy science and technology"},{"id":57714024,"name":"Physical sciences/Engineering"},{"id":57714025,"name":"Physical sciences/Mathematics and computing"}],"tags":[],"updatedAt":"2026-04-27T16:02:57+00:00","versionOfRecord":{"articleIdentity":"rs-7944519","link":"https://doi.org/10.1038/s41598-026-49094-x","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2026-04-24 15:59:08","publishedOnDateReadable":"April 24th, 2026"},"versionCreatedAt":"2025-11-11 08:51:50","video":"","vorDoi":"10.1038/s41598-026-49094-x","vorDoiUrl":"https://doi.org/10.1038/s41598-026-49094-x","workflowStages":[]},"version":"v1","identity":"rs-7944519","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7944519","identity":"rs-7944519","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}