{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,27]],"date-time":"2026-08-27T18:23:06Z","timestamp":1787854986286,"version":"build-2784847793"},"reference-count":69,"publisher":"Public Library of Science (PLoS)","issue":"8","license":[{"start":{"date-parts":[[2026,8,27]],"date-time":"2026-08-27T00:00:00Z","timestamp":1787788800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"General Project of Basic Scientific Research of Liaoning Provincial Department of Education","award":["JYTQN2023063"],"award-info":[{"award-number":["JYTQN2023063"]}]}],"content-domain":{"domain":["www.plosone.org"],"crossmark-restriction":false},"short-container-title":["PLoS One"],"abstract":"<jats:p>Remote sensing object detection faces three challenges: extreme scale variation, arbitrary rotational orientations, and complex intermingled backgrounds. Although fusion of Convolutional Neural Networks (CNNs) and Transformers combines spatial precision with global modeling, it faces two limitations: (i) feature distribution misalignment between modalities, and (ii) ineffective exploitation of complementary discriminative strengths. To address these issues, we introduce DAAF (Dual-stream Adaptive Attention Fusion), a three-stage fusion framework that systematically aligns and integrates CNN and Transformer features. DAAF comprises three components: (1) The Unified Feature Distribution Calibration (UFDC) module applies instance normalization to align feature distributions, reducing Kullback-Leibler (KL) divergence by 94%; (2) The Heterogeneous Channel-wise Synergistic Selection (HCSS) module employs independent attention pathways for each stream, validated by zero overlap in top-10 channel weights between CNN and Transformer branches; (3) The Spatially Adaptive Discriminative-Preserving Gate (SADPG) module employs pixel-wise gating to adaptively balance fused and single-branch features. Experiments on three benchmarks (NWPU VHR-10, DOTA v1.0, DIOR) show consistent improvements averaging +2.05 percentage points (pp) in mean Average Precision (mAP) over the Add Fusion baseline, with substantial gains on structurally complex categories in NWPU (Bridge:\u2009+\u200917.36pp, Vehicle:\u2009+\u200913.18pp). These improvements are achieved with only 206K additional parameters (0.37% increase over baseline) and 7.2% latency rise compared to the baseline fusion method, demonstrating favorable efficiency for practical deployment.<\/jats:p>","DOI":"10.1371\/journal.pone.0353309","type":"journal-article","created":{"date-parts":[[2026,8,27]],"date-time":"2026-08-27T17:49:16Z","timestamp":1787852956000},"page":"e0353309","update-policy":"https:\/\/doi.org\/10.1371\/journal.pone.corrections_policy","source":"Crossref","is-referenced-by-count":0,"title":["DAAF: Dual-stream adaptive attention fusion with distribution alignment for remote sensing object detection"],"prefix":"10.1371","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0009-0002-8926-3722","authenticated-orcid":true,"given":"Mo","family":"Zhou","sequence":"first","affiliation":[{"name":"School of Information Science and Engineering, Shenyang Ligong University","place":["Shenyang, Liaoning, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yue","family":"Zhou","sequence":"additional","affiliation":[{"name":"School of Information Science and Engineering, Shenyang Ligong University","place":["Shenyang, Liaoning, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8735-1609","authenticated-orcid":true,"given":"Kai","family":"Song","sequence":"additional","affiliation":[{"name":"Liaoning Finance and Trade College","place":["Huludao, Liaoning, China"],"department":["School of Big Data"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"340","published-online":{"date-parts":[[2026,8,27]]},"reference":[{"issue":"2","key":"pone.0353309.ref001","doi-asserted-by":"crossref","first-page":"327","DOI":"10.3390\/rs16020327","article-title":"Remote Sensing Object Detection in the Deep Learning Era\u2014A Review","volume":"16","author":"S Gui","year":"2024","journal-title":"Remote Sens"},{"issue":"10","key":"pone.0353309.ref002","doi-asserted-by":"crossref","first-page":"620","DOI":"10.3390\/drones7100620","article-title":"Real-Time Object Detection Based on UAV Remote Sensing: A Systematic Literature Review","volume":"7","author":"Z Cao","year":"2023","journal-title":"Drones"},{"issue":"17","key":"pone.0353309.ref003","doi-asserted-by":"crossref","first-page":"5496","DOI":"10.3390\/s24175496","article-title":"Drone-DETR: Efficient Small Object Detection for Remote Sensing Image Using Enhanced RT-DETR Model","volume":"24","author":"Y Kong","year":"2024","journal-title":"Sensors (Basel)"},{"key":"pone.0353309.ref004","doi-asserted-by":"crossref","first-page":"112067","DOI":"10.1016\/j.ecolind.2024.112067","article-title":"Deep artificial intelligence applications for natural disaster management systems: A methodological review","volume":"163","author":"A Akhyar","year":"2024","journal-title":"Ecol Indic"},{"issue":"8","key":"pone.0353309.ref005","doi-asserted-by":"crossref","first-page":"2344","DOI":"10.3390\/buildings14082344","article-title":"Integrating Machine Learning and Remote Sensing in Disaster Management: A Decadal Review of Post-Disaster Building Damage Assessment","volume":"14","author":"S Al Shafian","year":"2024","journal-title":"Buildings"},{"issue":"2","key":"pone.0353309.ref006","doi-asserted-by":"crossref","first-page":"33","DOI":"10.37661\/1816-0301-2025-22-2-33-47","article-title":"Efficient detection of building in remote sensing images using an improved YOLOv10 network","volume":"22","author":"X Wu","year":"2025","journal-title":"Informatika (Minsk)"},{"issue":"4","key":"pone.0353309.ref007","article-title":"Detection of specific building in remote sensing images using a novel YOLO-S-CIOU model. 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