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However, in complex scenes, there is a tendency for false alarms (FAs) and misdetections to occur at a higher rate. To solve these problems, we propose a lightweight infrared small target detection algorithm LDHD\u2010Net. First, we design a novel Ghost\u2010Shuffle module in the backbone network to enhance the network feature extraction capability. Meanwhile, we remove redundant layers from the network to make the backbone network more lightweight. Second, we design a hierarchical attention enhancement module in the neck network to improve the saliency of UAV targets and reduce background noise interference. In addition, we design a novel small target detection structure and prediction heads in the shallow layers of the network to improve small target detection accuracy. Finally, we design a novel attention dual\u2010branch head to reduce interference between different tasks and improve the real\u2010time performance of algorithm detection. The experimental results show that compared with the original model, inference time remains essentially the same, LDHD\u2010Net parameters are only 3.9\u2009M and AP improves by 12.6%. Compared to SOTA methods, LDHD\u2010Net shows better performance on SIDD and Anti\u2010UAV410 datasets. The algorithm effectively improves the accuracy and real\u2010time detection of UAVs in complex scenes.<\/jats:p>","DOI":"10.1155\/2024\/7259029","type":"journal-article","created":{"date-parts":[[2024,10,25]],"date-time":"2024-10-25T15:21:54Z","timestamp":1729869714000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["LDHD\u2010Net: A Lightweight Network With Double Branch Head for Feature Enhancement of UAV Targets in Complex Scenes"],"prefix":"10.1155","volume":"2024","author":[{"given":"Cong","family":"Zhang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-2749-2250","authenticated-orcid":false,"given":"Qi","family":"Gao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rui","family":"Shi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mingkai","family":"Yue","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2024,10,25]]},"reference":[{"key":"e_1_2_10_1_2","doi-asserted-by":"publisher","DOI":"10.1109\/comst.2019.2902862"},{"key":"e_1_2_10_2_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.cja.2021.04.025"},{"key":"e_1_2_10_3_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.isprsjprs.2021.12.006"},{"key":"e_1_2_10_4_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.isprsjprs.2023.03.020"},{"key":"e_1_2_10_5_2","doi-asserted-by":"publisher","DOI":"10.1109\/tmc.2022.3162892"},{"key":"e_1_2_10_6_2","doi-asserted-by":"publisher","DOI":"10.1049\/sil2.12133"},{"key":"e_1_2_10_7_2","doi-asserted-by":"publisher","DOI":"10.1049\/ipr2.12523"},{"key":"e_1_2_10_8_2","doi-asserted-by":"publisher","DOI":"10.1109\/jproc.2023.3238524"},{"key":"e_1_2_10_9_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.dsp.2022.103514"},{"key":"e_1_2_10_10_2","article-title":"Faster R-CNN: Towards Real-Time Object Detection With Region Proposal Networks","volume":"28","author":"Ren S.","year":"2015","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_2_10_11_2","doi-asserted-by":"crossref","unstructured":"HeK. 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