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Recent advances in convolutional neural networks (CNNs) have demonstrated that attention mechanism remarkably enhances multiscale representation of CNNs. However, most existing multiscale feature representation methods simply employ several attention blocks in the attention mechanism to adaptively recalibrate the feature response, which overlooks the context information at a multiscale level. To solve this problem, a multiscale feature filtering network (MFFNet) is proposed in this paper for image recognition system in the UAV. A novel building block, namely, multiscale feature filtering (MFF) module, is proposed for ResNet\u2010like backbones and it allows feature\u2010selective learning for multiscale context information across multiparallel branches. These branches employ multiple atrous convolutions at different scales, respectively, and further adaptively generate channel\u2010wise feature responses by emphasizing channel\u2010wise dependencies. Experimental results on CIFAR100 and Tiny ImageNet datasets reflect that the MFFNet achieves very competitive results in comparison with previous baseline models. Further ablation experiments verify that the MFFNet can achieve consistent performance gains in image classification and object detection tasks.<\/jats:p>","DOI":"10.1155\/2021\/6663851","type":"journal-article","created":{"date-parts":[[2021,2,20]],"date-time":"2021-02-20T02:59:39Z","timestamp":1613789979000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Multiscale Feature Filtering Network for Image Recognition System in Unmanned Aerial Vehicle"],"prefix":"10.1155","volume":"2021","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5805-9844","authenticated-orcid":false,"given":"Xianghua","family":"Ma","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6377-6629","authenticated-orcid":false,"given":"Zhenkun","family":"Yang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shining","family":"Chen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2021,2,19]]},"reference":[{"key":"e_1_2_8_1_2","doi-asserted-by":"publisher","DOI":"10.1109\/LGRS.2018.2790426"},{"key":"e_1_2_8_2_2","doi-asserted-by":"publisher","DOI":"10.1109\/lgrs.2017.2671922"},{"key":"e_1_2_8_3_2","first-page":"1097","volume-title":"Processing Advances in Neural Information Processing Systems","author":"Krizhevsky A.","year":"2012"},{"key":"e_1_2_8_4_2","doi-asserted-by":"crossref","unstructured":"ZhangX. 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