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Multispectral fusion addresses this limitation by leveraging complementary information from both modalities; however, existing methods predominantly rely on intricate fusion modules to integrate cross-modal features, inevitably incurring significant computational overhead and architectural complexity. To mitigate this issue, we propose a novel Cross-modal Orthogonal Representation Enhancement Network (CORE-Net). Diverging from conventional heavy-fusion paradigms, our framework adopts a dual-branch architecture integrated with a streamlined Cross-modal Concatenation Network Framework (CCNF), which achieves efficient feature integration while substantially reducing model complexity. Furthermore, CORE-Net incorporates two distinct components\u2014the Multiple Pooling Convolution Downsampling (MPCD) module and the Refined Integration Network (RINet)\u2014specifically designed to optimize feature extraction capabilities. Extensive evaluations on the DroneVehicle and LLVIP datasets demonstrate that CORE-Net achieves state-of-the-art (SOTA) performance in terms of both detection accuracy and computational efficiency. Ablation studies substantiate the individual and synergistic contributions of each proposed component, while deployment on edge devices further corroborates the model\u2019s practical efficiency. Additionally, qualitative visualizations confirm the model\u2019s efficacy in suppressing background noise and enhancing discriminative fine-grained features. In summary, CORE-Net establishes a robust new paradigm for high-performance and efficient multispectral object detection.<\/jats:p>","DOI":"10.1371\/journal.pone.0340499","type":"journal-article","created":{"date-parts":[[2026,4,21]],"date-time":"2026-04-21T17:54:32Z","timestamp":1776794072000},"page":"e0340499","update-policy":"https:\/\/doi.org\/10.1371\/journal.pone.corrections_policy","source":"Crossref","is-referenced-by-count":2,"title":["CORE-Net: A cross-modal orthogonal representation enhancement network for low-altitude multispectral object detection"],"prefix":"10.1371","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0009-0003-0001-0049","authenticated-orcid":true,"given":"Daoze","family":"Tang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-3438-9404","authenticated-orcid":true,"given":"Shuyun","family":"Tang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6431-360X","authenticated-orcid":true,"given":"Dequan","family":"Zheng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"340","published-online":{"date-parts":[[2026,4,21]]},"reference":[{"issue":"4","key":"pone.0340499.ref001","doi-asserted-by":"crossref","first-page":"8","DOI":"10.1109\/MGRS.2023.3312347","article-title":"Remote sensing object detection meets deep learning: A metareview of challenges and advances","volume":"11","author":"X Zhang","year":"2023","journal-title":"IEEE Geosci Remote Sens Mag."},{"key":"pone.0340499.ref002","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/TGRS.2023.3275753","article-title":"Novel adaptive region spectral\u2013spatial features for land cover classification with high spatial resolution remotely sensed imagery","volume":"61","author":"Z Lv","year":"2023","journal-title":"IEEE Trans Geosci Remote Sensing."},{"key":"pone.0340499.ref003","doi-asserted-by":"crossref","first-page":"105103","DOI":"10.1016\/j.autcon.2023.105103","article-title":"Small object detection (SOD) system for comprehensive construction site safety monitoring","volume":"156","author":"S Kim","year":"2023","journal-title":"Automat Construct."},{"issue":"1","key":"pone.0340499.ref004","doi-asserted-by":"crossref","first-page":"675","DOI":"10.1109\/TGRS.2020.2991407","article-title":"Automatic weakly supervised object detection from high spatial resolution remote sensing images via dynamic curriculum learning","volume":"59","author":"X Yao","year":"2021","journal-title":"IEEE Trans Geosci Remote Sensing."},{"issue":"19","key":"pone.0340499.ref005","doi-asserted-by":"crossref","first-page":"7167","DOI":"10.3390\/s22197167","article-title":"An efficient and uncertainty-aware decision support system for disaster response using aerial imagery","volume":"22","author":"J Bin","year":"2022","journal-title":"Sensors (Basel)."},{"issue":"8","key":"pone.0340499.ref006","doi-asserted-by":"crossref","first-page":"1532","DOI":"10.1109\/TPAMI.2014.2300479","article-title":"Fast feature pyramids for object detection","volume":"36","author":"P Dollar","year":"2014","journal-title":"IEEE Trans Pattern Anal Mach Intell."},{"key":"pone.0340499.ref007","doi-asserted-by":"crossref","unstructured":"Girshick R. 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