{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,3]],"date-time":"2026-03-03T16:08:16Z","timestamp":1772554096688,"version":"3.50.1"},"reference-count":60,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2025,4,25]],"date-time":"2025-04-25T00:00:00Z","timestamp":1745539200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Research Fund of the Key Laboratory for Civil Aviation Data Governance and Decision Optimization","award":["CAMICCADGDO-2025-(01-01)"],"award-info":[{"award-number":["CAMICCADGDO-2025-(01-01)"]}]},{"name":"Research Fund of the Key Laboratory for Civil Aviation Data Governance and Decision Optimization","award":["FZ2022ZZ01"],"award-info":[{"award-number":["FZ2022ZZ01"]}]},{"name":"Key Laboratory of Flight Techniques and Flight Safety, CAAC","award":["CAMICCADGDO-2025-(01-01)"],"award-info":[{"award-number":["CAMICCADGDO-2025-(01-01)"]}]},{"name":"Key Laboratory of Flight Techniques and Flight Safety, CAAC","award":["FZ2022ZZ01"],"award-info":[{"award-number":["FZ2022ZZ01"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Symmetry"],"abstract":"<jats:p>Oriented objects in RSI (Remote Sensing Imagery) typically present arbitrary rotations, extreme aspect ratios, multi-scale variations, and complex backgrounds. These factors often result in feature misalignment, representational ambiguity, and regression inconsistency, which significantly degrade detection performance. To address these issues, GAANet (Gaussian-Augmented Additive Network), a symmetry-driven framework for ODD (oriented object detection), is proposed. GAANet incorporates a symmetry-preserving mechanism into three critical components\u2014feature extraction, representation modeling, and metric optimization\u2014facilitating systematic improvements from structural representation to learning objectives. A CAX-ViT (Contextual Additive Exchange Vision Transformer) is developed to enhance multi-scale structural modeling by combining spatial\u2013channel symmetric interactions with convolution\u2013attention fusion. A GBBox (Gaussian Bounding Box) representation is employed, which implicitly encodes directional information through the invariance of the covariance matrix, thereby alleviating angular periodicity problems. Additionally, a GPIoU (Gaussian Product Intersection over Union) loss function is introduced to ensure geometric consistency between training objectives and the SkewIoU evaluation metric. GAANet achieved a 90.58% mAP on HRSC2016, 89.95% on UCAS-AOD, and 77.86% on the large-scale DOTA v1.0 dataset, outperforming mainstream methods across various benchmarks. In particular, GAANet showed a +3.27% mAP improvement over R3Det and a +4.68% gain over Oriented R-CNN on HRSC2016, demonstrating superior performance over representative baselines. Overall, GAANet establishes a closed-loop detection paradigm that integrates feature interaction, probabilistic modeling, and metric optimization under symmetry priors, offering both theoretical rigor and practical efficacy.<\/jats:p>","DOI":"10.3390\/sym17050653","type":"journal-article","created":{"date-parts":[[2025,4,25]],"date-time":"2025-04-25T11:47:41Z","timestamp":1745581661000},"page":"653","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["GAANet: Symmetry-Driven Gaussian Modeling with Additive Attention for Precise and Robust Oriented Object Detection"],"prefix":"10.3390","volume":"17","author":[{"ORCID":"https:\/\/orcid.org\/0009-0003-5128-5376","authenticated-orcid":false,"given":"Jiangang","family":"Zhu","sequence":"first","affiliation":[{"name":"School of Computer Science, Civil Aviation Flight University of China, Guanghan 618307, China"},{"name":"Key Laboratory for Civil Aviation Data Governance and Decision Optimization, Civil Aviation Management Institute of China, Beijing 100102, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3411-2193","authenticated-orcid":false,"given":"Yi","family":"Liu","sequence":"additional","affiliation":[{"name":"Key Laboratory for Civil Aviation Data Governance and Decision Optimization, Civil Aviation Management Institute of China, Beijing 100102, China"}]},{"given":"Qiang","family":"Fu","sequence":"additional","affiliation":[{"name":"School of Computer Science, Civil Aviation Flight University of China, Guanghan 618307, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3021-5371","authenticated-orcid":false,"given":"Donglin","family":"Jing","sequence":"additional","affiliation":[{"name":"School of Information and Electronics, Beijing Institute of Technology, Beijing 100081, China"}]}],"member":"1968","published-online":{"date-parts":[[2025,4,25]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"324","DOI":"10.5220\/0006120603240331","article-title":"A high resolution optical satellite image dataset for ship recognition and some new baselines","volume":"Volume 2","author":"Liu","year":"2017","journal-title":"Proceedings of the 6th International Conference on Pattern Recognition Applications and Methods (ICPRAM 2017)"},{"key":"ref_2","first-page":"4898","article-title":"Understanding the effective receptive field in deep convolutional neural networks","volume":"29","author":"Luo","year":"2016","journal-title":"Adv. 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