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As a downstream task of the general object detection, there are some differences between the vehicle detection in aerial images and the general object detection in ground view images, e.g., larger image areas, smaller target sizes, and more complex background. In this paper, to improve the performance of this task, a Dense Attentional Residual Network (DAR\u2010Net) is proposed. The proposed network employs a novel dense waterfall residual block (DW res\u2010block) to effectively preserve the spatial information and extract high\u2010level semantic information at the same time. A multiscale receptive field attention (MRFA) module is also designed to select the informative feature from the feature maps and enhance the ability of multiscale perception. Based on the DW res\u2010block and MRFA module, to protect the spatial information, the proposed framework adopts a new backbone that only downsamples the feature map 3 times; i.e., the total downsampling ratio of the proposed backbone is 8. These designs could alleviate the degradation problem, improve the information flow, and strengthen the feature reuse. In addition, deep\u2010projection units are used to reduce the impact of information loss caused by downsampling operations, and the identity mapping is applied to each stage of the proposed backbone to further improve the information flow. The proposed DAR\u2010Net is evaluated on VEDAI, UCAS\u2010AOD, and DOTA datasets. The experimental results demonstrate that the proposed framework outperforms other state\u2010of\u2010the\u2010art algorithms.<\/jats:p>","DOI":"10.1155\/2021\/6340823","type":"journal-article","created":{"date-parts":[[2021,11,26]],"date-time":"2021-11-26T17:05:10Z","timestamp":1637946310000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["DAR\u2010Net: Dense Attentional Residual Network for Vehicle Detection in Aerial Images"],"prefix":"10.1155","volume":"2021","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1733-9839","authenticated-orcid":false,"given":"Kaifeng","family":"Li","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3415-7885","authenticated-orcid":false,"given":"Bin","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2021,11,26]]},"reference":[{"key":"e_1_2_9_1_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2020.01.085"},{"key":"e_1_2_9_2_2","article-title":"Sift-the scale invariant feature transform","volume":"2","author":"Lowe G.","year":"2004","journal-title":"International Journal"},{"key":"e_1_2_9_3_2","unstructured":"DalalN.andTriggsB. 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