{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,19]],"date-time":"2026-08-19T04:28:20Z","timestamp":1787113700651,"version":"build-2736575974"},"reference-count":53,"publisher":"MDPI AG","issue":"16","license":[{"start":{"date-parts":[[2026,8,17]],"date-time":"2026-08-17T00:00:00Z","timestamp":1786924800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["61672210"],"award-info":[{"award-number":["61672210"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"award":["61672210"],"award-info":[{"award-number":["61672210"]}],"id":[{"id":"https:\/\/ror.org\/01h0zpd94","id-type":"ROR","asserted-by":"publisher"}]},{"name":"Major Science and Technology Program of Henan Province","award":["221100210500"],"award-info":[{"award-number":["221100210500"]}]},{"name":"Central Government Guiding Local Science and Technology Development Fund Program of Henan Province","award":["Z20221343032"],"award-info":[{"award-number":["Z20221343032"]}]},{"name":"Provincial Science and Technology Research and Development Plan Joint Fund Program of Henan Province","award":["245200810016"],"award-info":[{"award-number":["245200810016"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Oriented object detection in high-resolution remote sensing images remains vulnerable to object\u2013background confusion in complex scenes, where visually similar background structures can produce high-confidence false positives. To address this problem, we propose BGD-Net, an object\u2013background decoupling framework that suppresses confusing background responses from the feature, sample, and optimization levels. First, a Background-Decoupled Feature Module (BDFM) separately models object responses and background activations and performs residual feature purification before proposal generation. Second, Hard Background Mining (HBM) identifies high-confidence, low-overlap background RoIs that are most likely to cause false-positive detections and assigns them greater training emphasis. Third, an Object\u2013Background Contrastive Suppression Loss (OBCS Loss) uses the mined hard backgrounds as targeted negative samples to enlarge the representation gap between true objects and confusing backgrounds in the RoI embedding space. Experiments on DOTA-v1.0 show that BGD-Net achieves 82.94% mAP@0.5, improving the Oriented R-CNN baseline by 2.07 percentage points while reducing Hard-FP by 38.48%. The method also achieves 98.47% mAP@0.5 on HRSC2016 and 68.35% mAP@0.5 on DIOR-R, demonstrating effective performance across different remote sensing scenes. Ablation, multi-seed, and threshold-sensitivity experiments further verify the complementary contributions and reproducibility of the proposed components. BGD-Net introduces only a modest increase in model complexity, indicating a favorable balance between detection accuracy and practical efficiency.<\/jats:p>","DOI":"10.3390\/s26165193","type":"journal-article","created":{"date-parts":[[2026,8,17]],"date-time":"2026-08-17T11:00:05Z","timestamp":1786964405000},"page":"5193","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["BGD-Net: An Object\u2013Background Decoupling Network for Oriented Object Detection in Complex Remote Sensing Scenes"],"prefix":"10.3390","volume":"26","author":[{"ORCID":"https:\/\/orcid.org\/0009-0003-5321-3202","authenticated-orcid":false,"given":"Jiaxin","family":"Xu","sequence":"first","affiliation":[{"name":"College of Information Engineering and Artificial Intelligence, Henan University of Science and Technology, Luoyang 471000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9545-5443","authenticated-orcid":false,"given":"Hua","family":"Huo","sequence":"additional","affiliation":[{"name":"College of Information Engineering and Artificial Intelligence, Henan University of Science and Technology, Luoyang 471000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-7919-5550","authenticated-orcid":false,"given":"Aokun","family":"Mei","sequence":"additional","affiliation":[{"name":"College of Information Engineering and Artificial Intelligence, Henan University of Science and Technology, Luoyang 471000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chen","family":"Zhang","sequence":"additional","affiliation":[{"name":"College of Information Engineering and Artificial Intelligence, Henan University of Science and Technology, Luoyang 471000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,8,17]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Ding, J., Xue, N., Long, Y., Xia, G.S., and Lu, Q. 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