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Although the existing anchor\u2010free methods have fast inference speed, they are not suitable for object detection in crowded scenes due to the model's inability to predict the well\u2010fined object detection bounding boxes. This work proposes an end\u2010to\u2010end anchor\u2010free network, Multi\u2010dimensional Weighted Cross\u2010Attention Network (MANet), which can perform real\u2010time human detection in crowded scenes. Specifically, the Double\u2010flow Weighted Feature Cascade Module (DW\u2010FCM) is used in the extractor to highlight the contribution of features at different levels. The Triplet Cross Attention Module (TCAM) is used in the detector head to enhance the association dependence of multi\u2010dimension features, further strengthening human boundary features' discrimination ability at a fine\u2010grained level. Moreover, the strategy of Adaptively Opposite Thrust Mapping (AOTM) ground\u2010truth annotation is proposed to achieve bias correction of erroneous mappings and reduce the iterations of useless learning of the network. These strategies effectively alleviate the defect that the existing anchor\u2010free network cannot correctly distinguish and locate the individual human in crowded scenes. Compared with the anchor\u2010based detection method, there is no need to set anchor parameters manually, and the detection speed can satisfy the real\u2010time application. Finally, through extensive comparative experiments on CrowdHuman and WIDER FACE datasets, the results demonstrate that the improved strategy achieves the state\u2010of\u2010the\u2010art result in the anchor\u2010free\u00a0methods.<\/jats:p>","DOI":"10.1049\/ipr2.12298","type":"journal-article","created":{"date-parts":[[2021,7,2]],"date-time":"2021-07-02T00:17:10Z","timestamp":1625185030000},"page":"3585-3598","update-policy":"https:\/\/linproxy.fan.workers.dev:443\/https\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Multi\u2010dimensional weighted cross\u2010attention network in crowded scenes"],"prefix":"10.1049","volume":"15","author":[{"ORCID":"https:\/\/linproxy.fan.workers.dev:443\/https\/orcid.org\/0000-0001-9389-8902","authenticated-orcid":false,"given":"Yefan","family":"Xie","sequence":"first","affiliation":[{"name":"School of Computer Science and Engineering Northwestern Polytechnical University Xi'an PR China"},{"name":"Shaanxi Provincial Key Laboratory of Speech and Image Information Processing  Xi'an PR China"}]},{"given":"Jiangbin","family":"Zheng","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering Northwestern Polytechnical University Xi'an PR China"},{"name":"Shaanxi Provincial Key Laboratory of Speech and Image Information Processing  Xi'an PR China"}]},{"given":"Xuan","family":"Hou","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering Northwestern Polytechnical University Xi'an PR China"},{"name":"Shaanxi Provincial Key Laboratory of Speech and Image Information Processing  Xi'an PR China"}]},{"given":"Irfan Raza","family":"Naqvi","sequence":"additional","affiliation":[{"name":"School of Software Northwestern Polytechnical University Xi'an PR China"}]},{"given":"Yue","family":"Xi","sequence":"additional","affiliation":[{"name":"Aeronautics Engineering College Air Force Engineering University of PLA Xi'an China"}]},{"given":"Nailiang","family":"Kuang","sequence":"additional","affiliation":[{"name":"Xi'an Microelectronics Technology Institute Xi'an PR China"}]}],"member":"265","published-online":{"date-parts":[[2021,7]]},"reference":[{"key":"e_1_2_7_2_1","doi-asserted-by":"crossref","unstructured":"Liu S. et\u00a0al.:Adaptive NMS: Refining pedestrian detection in a crowd. 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