PowerLiteNet: A Lightweight Anomaly Detection Model for Powerline Transmission Infrastructure

PowerLiteNet: A Lightweight Anomaly Detection Model for Powerline Transmission Infrastructure | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL This is a preprint and has not been peer reviewed. Data may be preliminary. 31 March 2026 V1 Latest version Share on PowerLiteNet: A Lightweight Anomaly Detection Model for Powerline Transmission Infrastructure Authors : Andrews Danyo 0009-0008-3463-0349 [email protected] , Blessing Agyei Kyem , Anthony Dontoh , Joshua Kofi Asamoah 0009-0002-3258-0479 , and Armstrong Aboah Authors Info & Affiliations https://doi.org/10.22541/au.177497138.82292644/v1 126 views 109 downloads Contents Abstract Supplementary Material Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract Anomaly detection in power transmission infrastructure prevents failures and ensures grid reliability. Recent methods have demon- strated excellent accuracy but struggle with computational efficiency when deployed on resource-constrained devices such as UAVs. Memory-based approaches with dominant performance like PatchCore require external memory banks that significantly increase execution time, while heavyweight normalizing flow models demand substantial computational resources. This paper introduces PowerLiteNet, a lightweight anomaly detection framework that maintains high detection capability while drastically reducing computational requirements. By integrating Squeeze-and-Excitation (SENet) attention with our lightweight architecture, our approach achieves a 57% reduction in computational demands and sub-millisecond inference times (0.6ms versus 179ms). Eval- uated on the Inspection Power Line Asset Dataset (InsPLAD), our SENet-enhanced lightweight model achieved significantly better performance (81.99% mean AUROC) compared to their non-enhanced models (76.69%). This research bridges the gap between advanced deep learning techniques and real-world deployability by balancing computational efficiency with detection accuracy. Our approach offers a scalable automated powerline asset monitoring solution on UAV platforms with limited computational resources. Supplementary Material File (algorithm.sty) Download 3.17 KB File (algorithmicx.sty) Download 26.12 KB File (amssymb.sty) Download 13.52 KB File (appendix.sty) Download 3.39 KB File (lettersp.sty) Download 11.88 KB File (main-body.tex) Download 62.15 KB File (mla.sty) Download 11.48 KB File (natbib.sty) Download 44.43 KB File (njdapacite.sty) Download 70.48 KB File (njdnatbib.sty) Download 45.15 KB File (optimal-design-layout.pag) Download .02 KB File (powerlitenet a lightweight anomaly detection model forpowerline transmission infrastructure.pdf) Download 6.46 MB File (usg.cls) Download 93.19 KB File (wileynjd-chicago-lastoo.bst) Download 26.02 KB Information & Authors Information Version history V1 Version 1 31 March 2026 Copyright This work is licensed under a Non Exclusive No Reuse License. Keywords computer vision convolutional neural nets inspection insulators Authors Affiliations Andrews Danyo 0009-0008-3463-0349 [email protected] North Dakota State University View all articles by this author Blessing Agyei Kyem North Dakota State University View all articles by this author Anthony Dontoh The University of Memphis View all articles by this author Joshua Kofi Asamoah 0009-0002-3258-0479 North Dakota State University View all articles by this author Armstrong Aboah North Dakota State University View all articles by this author Metrics & Citations Metrics Article Usage 126 views 109 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Andrews Danyo, Blessing Agyei Kyem, Anthony Dontoh, et al. PowerLiteNet: A Lightweight Anomaly Detection Model for Powerline Transmission Infrastructure. Authorea . 31 March 2026. DOI: https://doi.org/10.22541/au.177497138.82292644/v1 If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download. For more information or tips please see 'Downloading to a citation manager' in the Help menu . Format Please select one from the list RIS (ProCite, Reference Manager) EndNote BibTex Medlars RefWorks Direct import Tips for downloading citations document.getElementById('citMgrHelpLink').addEventListener('click', function() { popupHelp(this.href); return false; }); $(".js__slcInclude").on("change", function(e){ if ($(this).val() == 'refworks') $('#direct').prop("checked", false); $('#direct').prop("disabled", ($(this).val() == 'refworks')); }); View Options View options PDF View PDF Figures Tables Media Share Share Share article link Copy Link Copied! Copying failed. 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