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However, datasets acquired from UAV platforms inevitably suffer from issues such as imbalanced class distribution, severe background interference, numerous small objects, and significant target scale variance, presenting substantial challenges to practical vehicle detection applications based on this platform. Addressing these challenges, this paper proposes an object detection model grounded in a background suppression pyramid network and multi-scale task adaptive decoupled head. Firstly, the model implements a long-tail feature resampling algorithm (LFRA) to solve the problem of imbalanced class distribution in the dataset. Next, a background suppression pyramid network (BSPN) is integrated into the Neck segment of the model. This network not only reduces the interference of redundant background information but also skillfully extracts features of small target vehicles, enhancing the ability of the model to detect small objects. Lastly, a multi-scale task adaptive decoupled head (MTAD) with varied receptive fields is introduced, enhancing detection accuracy by leveraging multi-scale features and adaptively generating relevant features for classification and detection. Experimental results indicate that the proposed model achieves state-of-the-art performance on lightweight object detection networks. Compared to the baseline model PP-YOLOE-s, our model improves the AP50:95 on the VisDrone-Vehicle dataset by 1.9%.<\/jats:p>","DOI":"10.3390\/rs15245698","type":"journal-article","created":{"date-parts":[[2023,12,12]],"date-time":"2023-12-12T05:23:22Z","timestamp":1702358602000},"page":"5698","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Vehicle Detection in UAV Images via Background Suppression Pyramid Network and Multi-Scale Task Adaptive Decoupled Head"],"prefix":"10.3390","volume":"15","author":[{"given":"Mian","family":"Pan","sequence":"first","affiliation":[{"name":"School of Electronic Information, Hangzhou Dianzi University, Hangzhou 310005, China"}]},{"given":"Weijie","family":"Xia","sequence":"additional","affiliation":[{"name":"School of Electronic Information, Hangzhou Dianzi University, Hangzhou 310005, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0900-5276","authenticated-orcid":false,"given":"Haibin","family":"Yu","sequence":"additional","affiliation":[{"name":"School of Electronic Information, Hangzhou Dianzi University, Hangzhou 310005, China"}]},{"given":"Xinzhi","family":"Hu","sequence":"additional","affiliation":[{"name":"School of Electronic Information, Hangzhou Dianzi University, Hangzhou 310005, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8858-9221","authenticated-orcid":false,"given":"Wenyu","family":"Cai","sequence":"additional","affiliation":[{"name":"School of Electronic Information, Hangzhou Dianzi University, Hangzhou 310005, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4148-6388","authenticated-orcid":false,"given":"Jianguang","family":"Shi","sequence":"additional","affiliation":[{"name":"School of Electronic Information, Hangzhou Dianzi University, Hangzhou 310005, China"}]}],"member":"1968","published-online":{"date-parts":[[2023,12,12]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"445","DOI":"10.1007\/978-3-319-46448-0_27","article-title":"A Benchmark and Simulator for UAV Tracking","volume":"Volume 9905","author":"Leibe","year":"2016","journal-title":"Computer Vision\u2014ECCV 2016: Proceedings of the 14th European Conference, Amsterdam, The Netherlands, 11\u201314 October 2016"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"549","DOI":"10.1007\/978-3-319-46484-8_33","article-title":"Learning Social Etiquette: Human Trajectory Understanding in Crowded Scenes","volume":"Volume 9912","author":"Leibe","year":"2016","journal-title":"Computer Vision\u2014ECCV 2016: Proceedings of the 14th European Conference, Amsterdam, The Netherlands, 11\u201314 October 2016"},{"key":"ref_3","unstructured":"Zhu, P., Wen, L., Bian, X., Ling, H., and Hu, Q. 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