TEA-DETR: A Texture-Edge Augmented Framework for Military Camouflaged People Detection

TEA-DETR: A Texture-Edge Augmented Framework for Military Camouflaged People 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 Article TEA-DETR: A Texture-Edge Augmented Framework for Military Camouflaged People Detection Lifang Chen, Xufeng Zhang, Mingxu Chen This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8171162/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 12 You are reading this latest preprint version Abstract Military Camouflaged People Detection (MCPD) aims to detect military personnel in natural environments, facing challenges from indistinct boundaries, deceptive textures, and limited high-quality datasets. Existing camouflaged object detection (COD) methods struggle with edge blur issues when distinguishing camouflaged soldiers from complex backgrounds. We propose Texture-Edge Augmented DEtection TRansformer (TEA-DETR) to address edge ambiguity through three innovations: (1) a multi-scale edge enhancement backbone extracting multi-resolution edge information with reduced computational overhead, (2) a Gabor-MaxPool Texture Enhancement Module utilizing Gabor filters' orientation selectivity and adaptive pooling for discriminative texture extraction while suppressing background noise, and (3) a feature fusion module integrating edge and texture features for comprehensive representations enhancing boundary attention. We develop a high-quality MCPD dataset providing realistic military scenario benchmarks. Experimental results show TEA-DETR achieves superior performance with 97.5% mAP50 on CPDD and 92.4% mAP50 on HCPD, significantly outperforming existing object detectors. Physical sciences/Engineering Physical sciences/Mathematics and computing Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Revision Version 1 posted Editorial decision: Revision requested 01 Jan, 2026 Reviews received at journal 31 Dec, 2025 Reviews received at journal 31 Dec, 2025 Reviews received at journal 24 Dec, 2025 Reviewers agreed at journal 08 Dec, 2025 Reviewers agreed at journal 08 Dec, 2025 Reviewers agreed at journal 04 Dec, 2025 Reviewers invited by journal 04 Dec, 2025 Editor assigned by journal 04 Dec, 2025 Editor invited by journal 28 Nov, 2025 Submission checks completed at journal 26 Nov, 2025 First submitted to journal 26 Nov, 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. 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Existing camouflaged object detection (COD) methods struggle with edge blur issues when distinguishing camouflaged soldiers from complex backgrounds. We propose Texture-Edge Augmented DEtection TRansformer (TEA-DETR) to address edge ambiguity through three innovations: (1) a multi-scale edge enhancement backbone extracting multi-resolution edge information with reduced computational overhead, (2) a Gabor-MaxPool Texture Enhancement Module utilizing Gabor filters' orientation selectivity and adaptive pooling for discriminative texture extraction while suppressing background noise, and (3) a feature fusion module integrating edge and texture features for comprehensive representations enhancing boundary attention. We develop a high-quality MCPD dataset providing realistic military scenario benchmarks. 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