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M2YOLOF: Based on effective receptive fields and multiple-in-single-out encoder for object detection

文献类型：期刊论文

作者Wang, Qijin1,2,3,4,5; Qian, Yu1; Hu, Yating1; Wang, Chao1; Ye, Xiaodong3; Wang, Hongqiang3,4
刊名EXPERT SYSTEMS WITH APPLICATIONS
出版日期2023-03-01
卷号213
ISSN号0957-4174
关键词Object detection Deep learning YOLOF Effective receptive field
DOI10.1016/j.eswa.2022.118928
通讯作者Wang, Hongqiang(hqwang@ustc.edu)
英文摘要Object detection under one-level feature is a difficult task, which requires that different scale object represen-tations can be extracted on one feature map, as well as the balance between quality and quantity of positive samples play a key role in model training. YOLOF with real-time detection speed solves the partial problems about object scale and sample quantity balance. To further improve performance especially in smaller objects, we propose a new object detector called M2YOLOF. The main ingredients are a multi-in-single-out encoder that joints attention to strengthen the local feature and global representation of each multi-scale object, and a dy-namic sample selection policy that using effective receptive fields to rationalize the quantity of positive samples. M2YOLOF strengthen the contextual details of feature map and balances the rationality of training samples. Extensive experiments on COCO benchmark prove the effectiveness of our method, with an image size of [1333,800], using ResNet50 as backbone, running at 29 FPS on 2080Ti and achieving 39.2 AP. It is 1.7 AP higher than YOLOF but GFLOPs of our method only increases by <9%.
资助项目National Natural Science Foundation of China ; Academic funding project for top talents of disciplines in Colleges and universities of Anhui Province ; [61973295] ; [61773360] ; [201904a07020092] ; [gxbjZD2020096]
WOS研究方向Computer Science ; Engineering ; Operations Research & Management Science
语种英语
出版者PERGAMON-ELSEVIER SCIENCE LTD
WOS记录号WOS:000867522400002
资助机构National Natural Science Foundation of China ; Academic funding project for top talents of disciplines in Colleges and universities of Anhui Province
源URL[http://ir.hfcas.ac.cn:8080/handle/334002/129428]  
专题中国科学院合肥物质科学研究院
通讯作者Wang, Hongqiang
作者单位1.Anhui Jianzhu Univ, Sch Elect & Informat Engn, Hefei, Peoples R China
2.Univ Sci & Technol China, Hefei, Peoples R China
3.Chinese Acad Sci, Inst Intelligent Machines, Hefei Inst Phys Sci, Hefei, Peoples R China
4.Chinese Acad Sci, Hefei Inst Phys Sci, Zhongqi AI Joint Lab, Hefei, Peoples R China
5.Anhui Xinhua Univ, Hefei, Peoples R China
推荐引用方式
GB/T 7714		 Wang, Qijin,Qian, Yu,Hu, Yating,et al. M2YOLOF: Based on effective receptive fields and multiple-in-single-out encoder for object detection[J]. EXPERT SYSTEMS WITH APPLICATIONS,2023,213.
APA Wang, Qijin,Qian, Yu,Hu, Yating,Wang, Chao,Ye, Xiaodong,&Wang, Hongqiang.(2023).M2YOLOF: Based on effective receptive fields and multiple-in-single-out encoder for object detection.EXPERT SYSTEMS WITH APPLICATIONS,213.
MLA Wang, Qijin,et al."M2YOLOF: Based on effective receptive fields and multiple-in-single-out encoder for object detection".EXPERT SYSTEMS WITH APPLICATIONS 213(2023).

入库方式： OAI收割

来源：合肥物质科学研究院

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