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However, the drawbacks in the design prevent the multiscale features from being completely exploited. This paper introduces a feature pyramid architecture to address this problem. In the proposed architecture, an improving region proposal network is designed to generate intermediate feature maps which are then used to add more discriminative representations to feature maps generated by the backbone network, as well as improving the computational cost of the network. To generate more discriminative feature representations, this paper introduces multilayer enhancement module to reweight feature representations of feature maps generated by the backbone network to increase the discrimination of foreground objects and background regions in each feature map. In addition, an adaptive RoI pooling module is proposed to pool features from all pyramid levels for each proposal and fuse them for the detection network. Experimental results on the KITTI vehicle detection benchmark and the PASCAL VOC 2007 car dataset show that the proposed approach obtains better detection performance compared with recent methods on vehicle detection.<\/jats:p>","DOI":"10.1155\/2021\/5555121","type":"journal-article","created":{"date-parts":[[2021,6,14]],"date-time":"2021-06-14T18:35:09Z","timestamp":1623695709000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Multiscale Feature Learning Based on Enhanced Feature Pyramid for Vehicle Detection"],"prefix":"10.1155","volume":"2021","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5798-7446","authenticated-orcid":false,"given":"Hoanh","family":"Nguyen","sequence":"first","affiliation":[]}],"member":"311","published-online":{"date-parts":[[2021,6,14]]},"reference":[{"key":"e_1_2_8_1_2","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2016.2577031"},{"key":"e_1_2_8_2_2","doi-asserted-by":"crossref","unstructured":"LiuW. 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