{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T01:43:55Z","timestamp":1760060635892,"version":"build-2065373602"},"reference-count":45,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2025,9,9]],"date-time":"2025-09-09T00:00:00Z","timestamp":1757376000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Fundamental Research Funds for the Central Universities","award":["3282025010","3282024058"],"award-info":[{"award-number":["3282025010","3282024058"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Symmetry"],"abstract":"<jats:p>Current mainstream remote sensing target detection algorithms mostly estimate the rotation angle of targets by designing different bounding box descriptions and loss functions. However, they fail to consider the symmetry\u2013asymmetry duality anisotropy in the distribution of key features required for target localization. Moreover, the equivalent feature extraction mode of shared convolutional kernels may lead to difficulties in accurately predicting parameters with different attributes, thereby reducing the performance of the detector. In this paper, we propose the Feature Equalization and Hierarchical Decoupling Network (FEHD-Net), which comprises three core components: a Symmetry-Enhanced Parallel Interleaved Convolution Module (PICM), a Parameter Decoupling Module (PDM), and a Critical Feature Matching Loss Function (CFM-Loss). PICM captures diverse spatial features over long distances by integrating square convolution and multi-branch continuous orthogonal large kernel strip convolution sequences, thereby enhancing the network\u2019s capability in processing long-distance spatial information. PDM decomposes feature maps with different properties and assigns them to different regression branches to estimate the parameters of the target\u2019s rotating bounding box. Finally, to stabilize the training of anchors with different qualities that have captured the key features required for detection, CFM-Loss utilizes the intersection ratio between anchors and true value labels, as well as the uncertainty of convolutional regression during training, and designs an alignment criterion (symmetry-aware alignment) to evaluate the regression ability of different anchors. This enables the network to fine-tune the processing of templates with different qualities, achieving stable training of the network. A large number of experiments demonstrate that compared with existing methods, FEHD-Net can achieve state-of-the-art performance on DOTA, HRSC2016, and UCAS-AOD datasets.<\/jats:p>","DOI":"10.3390\/sym17091491","type":"journal-article","created":{"date-parts":[[2025,9,9]],"date-time":"2025-09-09T08:40:15Z","timestamp":1757407215000},"page":"1491","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Feature Equalization and Hierarchical Decoupling Network for Rotated and High-Aspect-Ratio Object Detection"],"prefix":"10.3390","volume":"17","author":[{"given":"Wenbin","family":"Gao","sequence":"first","affiliation":[{"name":"School of Information and Engineering, Beijing Electronic Science and Technology Institute, Beijing 100070, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jinda","family":"Ji","sequence":"additional","affiliation":[{"name":"Shanghai Aerospace Control Technology Institute, Shanghai 201109, China"},{"name":"Research and Development Center of Infrared Detection Technology, China Aerospace Science and Technology Corporation, Shanghai 201109, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3021-5371","authenticated-orcid":false,"given":"Donglin","family":"Jing","sequence":"additional","affiliation":[{"name":"Shanghai Aerospace Control Technology Institute, Shanghai 201109, China"},{"name":"Research and Development Center of Infrared Detection Technology, China Aerospace Science and Technology Corporation, Shanghai 201109, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2025,9,9]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Han, J., Ding, J., Xue, N., and Xia, G.S. 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