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REMW‐YOLO: A Balanced Dense Pedestrian Detection Model With Efficient Feature Extraction and Fusion.

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Title: REMW‐YOLO: A Balanced Dense Pedestrian Detection Model With Efficient Feature Extraction and Fusion.
Authors: Yu, Hefeng1,2, Zu, Linan3, Pan, Xiaona1, Tang, Chao4, Jin, Ming Liang2, jinmingliang@qdu.edu.cn, Meng, Pingping1, mpp1979@qdu.edu.cn, Mohanta, Kallol, kmohanta@wiley.com
Source: International Journal of Intelligent Systems; 5/7/2026, Vol. 2026, p1-20, 20p
Database: Applied Science & Technology Source
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Header DbId: aci
DbLabel: Applied Science & Technology Source
An: 193599584
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PubTypeId: academicJournal
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  Data: REMW‐YOLO: A Balanced Dense Pedestrian Detection Model With Efficient Feature Extraction and Fusion.
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  Data: <searchLink fieldCode="AU" term="%22Yu%2C+Hefeng%22">Yu, Hefeng</searchLink><relatesTo>1,2</relatesTo><br /><searchLink fieldCode="AU" term="%22Zu%2C+Linan%22">Zu, Linan</searchLink><relatesTo>3</relatesTo><br /><searchLink fieldCode="AU" term="%22Pan%2C+Xiaona%22">Pan, Xiaona</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AU" term="%22Tang%2C+Chao%22">Tang, Chao</searchLink><relatesTo>4</relatesTo><br /><searchLink fieldCode="AU" term="%22Jin%2C+Ming+Liang%22">Jin, Ming Liang</searchLink><relatesTo>2</relatesTo>, <i>jinmingliang@qdu.edu.cn</i><br /><searchLink fieldCode="AU" term="%22Meng%2C+Pingping%22">Meng, Pingping</searchLink><relatesTo>1</relatesTo>, <i>mpp1979@qdu.edu.cn</i><br /><searchLink fieldCode="AU" term="%22Mohanta%2C+Kallol%22">Mohanta, Kallol</searchLink>, <i>kmohanta@wiley.com</i>
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      – Type: doi
        Value: 10.1155/int/2375277
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      – Code: eng
        Text: English
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        PageCount: 20
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      – TitleFull: REMW‐YOLO: A Balanced Dense Pedestrian Detection Model With Efficient Feature Extraction and Fusion.
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            NameFull: Yu, Hefeng
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            NameFull: Zu, Linan
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            NameFull: Pan, Xiaona
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            NameFull: Tang, Chao
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            NameFull: Jin, Ming Liang
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            NameFull: Meng, Pingping
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            – D: 07
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              Text: 5/7/2026
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              Y: 2026
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