{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,3]],"date-time":"2026-04-03T12:06:46Z","timestamp":1775218006798,"version":"3.50.1"},"reference-count":40,"publisher":"Institution of Engineering and Technology (IET)","issue":"1","license":[{"start":{"date-parts":[[2025,6,26]],"date-time":"2025-06-26T00:00:00Z","timestamp":1750896000000},"content-version":"vor","delay-in-days":176,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"},{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62263025"],"award-info":[{"award-number":["62263025"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["ietresearch.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["IET Image Processing"],"published-print":{"date-parts":[[2025,1]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:p>Deep neural networks deployed on UAVs have made significant progress in data acquisition in recent years. However, traditional algorithms and deep learning models still face challenges in small and unevenly distributed object detection tasks. To address this problem, we propose the MCD\u2010YOLOv10n model by introducing the MEMAttention module, which combines EMAttention with multiscale convolution, uses Softmax and AdaptiveAvgPool2d to adaptively compute feature weights, dynamically adjusts the region of interest, and captures cross\u2010scale features. In addition, the C2f_MEMAttention and C2f_DSConv modules are formed by the fusion of C2f with MEMAttention and DSConv, which enhances the model's ability of extracting and adapting to irregular target features. Experiments on three datasets, VisDrone\u2010DET2019, Exdark and DOTA\u2010v1.5, show that the evaluation metric mAP50 achieves the best detection accuracy of 32.9%, 52.9% and 68.2% when the number of holdout parameters is at the minimum value of 2.24M. Moreover, the mAP50\u201095 metrics (19.5% for VisDrone\u2010DET2019 and 45.0% for DOTA\u2010v1.5) are 1.1 and 1.2 percentage points ahead of the second place, respectively. In terms of Recall, the VisDrone\u2010DET2019 and DOTA\u2010v1.5 datasets improved by 1.0% and 0.7% over the baseline model. These results validate that MCD\u2010YOLOv10n has strong adaptability and generalization ability for small object detection in complex\u00a0scenes.<\/jats:p>","DOI":"10.1049\/ipr2.70145","type":"journal-article","created":{"date-parts":[[2025,6,27]],"date-time":"2025-06-27T01:39:41Z","timestamp":1750988381000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["MCD\u2010YOLOv10n: A Small Object Detection Algorithm for UAVs"],"prefix":"10.1049","volume":"19","author":[{"given":"Jinshuo","family":"Shi","sequence":"first","affiliation":[{"name":"Department of Electronic Information Engineering Inner Mongolia University Hohhot China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-2408-5753","authenticated-orcid":false,"given":"Xitai","family":"Na","sequence":"additional","affiliation":[{"name":"Department of Electronic Information Engineering Inner Mongolia University Hohhot China"}]},{"given":"Shiji","family":"Hai","sequence":"additional","affiliation":[{"name":"Department of Electronic Information Engineering Inner Mongolia University Hohhot China"}]},{"given":"Qingbin","family":"Sun","sequence":"additional","affiliation":[{"name":"Department of Electronic Information Engineering Inner Mongolia University Hohhot China"}]},{"given":"Zhihui","family":"Feng","sequence":"additional","affiliation":[{"name":"Department of Electronic Information Engineering Inner Mongolia University Hohhot China"}]},{"given":"Xinyang","family":"Zhu","sequence":"additional","affiliation":[{"name":"Department of Electronic Information Engineering Inner Mongolia University Hohhot China"}]}],"member":"265","published-online":{"date-parts":[[2025,6,26]]},"reference":[{"key":"e_1_2_9_2_1","doi-asserted-by":"publisher","DOI":"10.1109\/TNSM.2024.3424232"},{"key":"e_1_2_9_3_1","volume-title":"Advances in Neural Information Processing Systems","author":"Singh B.","year":"2018"},{"key":"e_1_2_9_4_1","doi-asserted-by":"publisher","DOI":"10.1109\/LGRS.2024.3507760"},{"key":"e_1_2_9_5_1","doi-asserted-by":"publisher","DOI":"10.1109\/JSTARS.2024.3409072"},{"key":"e_1_2_9_6_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2022.119132"},{"key":"e_1_2_9_7_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2022.108980"},{"key":"e_1_2_9_8_1","unstructured":"A.Dosovitskiy L.Beyer andA.Kolesnikov et\u00a0al. \u201cAn Image is Worth 16x16 Words: Transformers for Image Recognition at Scale \u201darXivabs\/2010.11929(2020)."},{"key":"e_1_2_9_9_1","doi-asserted-by":"publisher","DOI":"10.3390\/rs15184580"},{"key":"e_1_2_9_10_1","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2024.3510781"},{"key":"e_1_2_9_11_1","unstructured":"C.Li A.Zhou andA.Yao \u201cOmni\u2010Dimensional Dynamic Convolution \u201darXivabs\/2209.07947(2022)."},{"key":"e_1_2_9_12_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01234-2_1"},{"key":"e_1_2_9_13_1","unstructured":"Y.Liu Z.Shao andN.Hoffmann \u201cGlobal Attention Mechanism: Retain Information to Enhance Channel\u2010Spatial Interactions \u201dCoRRabs\/2112.05561(2021)."},{"key":"e_1_2_9_14_1","doi-asserted-by":"crossref","unstructured":"J.Redmon S.Divvala R.Girshick andA.Farhadi \u201cYou Only Look Once: Unified Real\u2010Time Object Detection \u201d in2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)(IEEE Computer Society 2016) 779\u2013788.","DOI":"10.1109\/CVPR.2016.91"},{"key":"e_1_2_9_15_1","doi-asserted-by":"crossref","unstructured":"M. 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