{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,12]],"date-time":"2025-11-12T09:06:59Z","timestamp":1762938419357,"version":"3.45.0"},"reference-count":56,"publisher":"Wiley","issue":"25-26","license":[{"start":{"date-parts":[[2025,9,30]],"date-time":"2025-09-30T00:00:00Z","timestamp":1759190400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"funder":[{"DOI":"10.13039\/501100003392","name":"Natural Science Foundation of Fujian Province","doi-asserted-by":"publisher","award":["2022J05257"],"award-info":[{"award-number":["2022J05257"]}],"id":[{"id":"10.13039\/501100003392","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Concurrency and Computation"],"published-print":{"date-parts":[[2025,11,30]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:p>This study addressed the challenges of small target detection in aerial imaging applications, including limited pixel coverage, weak feature representation, and complex background interference, by proposing a collaborative optimisation algorithm named HMMSC\u2010YOLO. Firstly, a CNN\u2010Transformer heterogeneous feature interaction network was constructed to mitigate high\u2010frequency information attenuation during hierarchical transmission of small targets. Secondly, a parameter\u2010shared dilated convolutional chain structure was designed, employing a weight\u2010reuse strategy across multi\u2010branch heterogeneous receptive fields to enhance geometric feature sensitivity towards minuscule targets. A differentiable affine transformation\u2010guided multi\u2010kernel dynamic fusion mechanism was further developed, achieving high\u2010precision geometric alignment of cross\u2010scale features through learnable deformation fields, thereby overcoming the rigid fusion limitations of conventional feature pyramids. A dual\u2010attention\u2010driven feature recalibration architecture was introduced to improve target localisation robustness under complex background interference. Finally, a dual\u2010path collaborative downsampling module was implemented to suppress feature confusion caused by traditional single\u2010path downsampling. Experimental evaluations on the VisDrone2019 dataset demonstrated 1.4% and 1% improvements in mAP50 and mAP50:95 metrics respectively compared to baseline models, alongside 23.3% and 2.5% reductions in parameter quantity and computational costs. The algorithm exhibited superior localisation accuracy and occlusion resistance in dense small target scenarios, establishing an innovative technical framework for practical applications including aerial image analysis and low\u2010light environmental monitoring.<\/jats:p>","DOI":"10.1002\/cpe.70288","type":"journal-article","created":{"date-parts":[[2025,10,1]],"date-time":"2025-10-01T05:55:42Z","timestamp":1759298142000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["<scp>HMMSC<\/scp>\n                    \u2010\n                    <scp>YOLO<\/scp>\n                    : A Comprehensively Improved Small Target Detection Algorithm"],"prefix":"10.1002","volume":"37","author":[{"ORCID":"https:\/\/orcid.org\/0009-0003-7846-1827","authenticated-orcid":false,"given":"Chongyang","family":"Fan","sequence":"first","affiliation":[{"name":"School of Electromechanical and Information Engineering Putian College  Putian Fujian Province China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wenfang","family":"Li","sequence":"additional","affiliation":[{"name":"School of Electromechanical and Information Engineering Putian College  Putian Fujian Province China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chang","family":"Lin","sequence":"additional","affiliation":[{"name":"School of Electromechanical and Information Engineering Putian College  Putian Fujian Province China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2025,9,30]]},"reference":[{"key":"e_1_2_7_2_1","doi-asserted-by":"publisher","DOI":"10.1016\/0146-664X(79)90001-7"},{"key":"e_1_2_7_3_1","doi-asserted-by":"publisher","DOI":"10.1080\/10106040108542184"},{"key":"e_1_2_7_4_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.imavis.2023.104697"},{"key":"e_1_2_7_5_1","doi-asserted-by":"publisher","DOI":"10.3390\/drones7030188"},{"key":"e_1_2_7_6_1","doi-asserted-by":"publisher","DOI":"10.3390\/s23167190"},{"issue":"2","key":"e_1_2_7_7_1","article-title":"Improving a Neural Network Model for Semantic Segmentation of Images of Monitored Objects in Aerial Photographs","volume":"6","author":"Slyusar V.","year":"2021","journal-title":"Eastern\u2010European Journal of Enterprise Technologies"},{"key":"e_1_2_7_8_1","doi-asserted-by":"publisher","DOI":"10.3390\/jimaging9100216"},{"key":"e_1_2_7_9_1","doi-asserted-by":"publisher","DOI":"10.1080\/01431161.2022.2051634"},{"key":"e_1_2_7_10_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2020.09.001"},{"issue":"1","key":"e_1_2_7_11_1","first-page":"111","article-title":"Delineating Landslide and Debris Flow Detection in Japan Through Aerial Photography: A YOLO v8 Approach to Disaster Management","volume":"5","author":"Opara J. 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