CASIA OpenIR  > 模式识别国家重点实验室  > 图像与视频分析
Food det: Detecting foods in refrigerator with supervised transformer network
Zhu, Yousong1,2; Zhao, Xu1,2; Zhao, Chaoyang1,2; Wang, Jinqiao1,2; Lu, Hanqing1,2
Source PublicationNEUROCOMPUTING
ISSN0925-2312
2020-02-28
Volume379Pages:162-171
Corresponding AuthorZhu, Yousong(yousong.zhu@nlpr.ia.ac.cn)
AbstractMost of existing methods mainly focus on the food image recognition which assumes that one food image contains only one food item. However, in this paper, we present a system to detect a diversity of foods in refrigerator where multiple food items may exist. In view of the refrigerator environment, we propose a food detection framework based on the supervised transformer network. More specifically, the supervised transformer network, dotted as RectNet, is first proposed to automatically select the irregular food regions and transform them to the frontal views. Then, based on the rectified food images, we further propose an end-to-end detection network that predicts the categories and locations of food items. The proposed detection network, called Lite Fully Convolutional Network (LiteFCN), is evolved from the advanced object detection algorithm Faster R-CNN while several significant improvements are tailored to achieve a higher accuracy and keep inference time efficiency. To validate the effectiveness of each component of our method, we build a real-world refrigerator dataset with 80 classes. Extensive experiments demonstrate that our methods achieve the state-of-the-art results, which improves the baseline by a large margin, e.g., 3-5% in terms of F-measure. We also show that the proposed detection network achieve a competitive result on the public PASCAL VOC2007 dataset, which outperforms the Faster R-CNN by 2.3% with a higher speed. (C) 2019 Elsevier B.V. All rights reserved.
KeywordFood detection Spatial transformer Object detection
DOI10.1016/j.neucom.2019.10.106
WOS KeywordCONTEXT
Indexed BySCI
Language英语
Funding ProjectNatural Science Foundation of China[61772527] ; Natural Science Foundation of China[61806200] ; Natural Science Foundation of China[61876086] ; Natural Science Foundation of China[61772527] ; Natural Science Foundation of China[61806200] ; Natural Science Foundation of China[61876086]
Funding OrganizationNatural Science Foundation of China
WOS Research AreaComputer Science
WOS SubjectComputer Science, Artificial Intelligence
WOS IDWOS:000507464700014
PublisherELSEVIER
Citation statistics
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/29488
Collection模式识别国家重点实验室_图像与视频分析
Corresponding AuthorZhu, Yousong
Affiliation1.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, 95 Zhongguancun East Rd, Beijing 100190, Peoples R China
2.Univ Chinese Acad Sci, Beijing 100049, Peoples R China
First Author AffilicationChinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
Corresponding Author AffilicationChinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
Recommended Citation
GB/T 7714
Zhu, Yousong,Zhao, Xu,Zhao, Chaoyang,et al. Food det: Detecting foods in refrigerator with supervised transformer network[J]. NEUROCOMPUTING,2020,379:162-171.
APA Zhu, Yousong,Zhao, Xu,Zhao, Chaoyang,Wang, Jinqiao,&Lu, Hanqing.(2020).Food det: Detecting foods in refrigerator with supervised transformer network.NEUROCOMPUTING,379,162-171.
MLA Zhu, Yousong,et al."Food det: Detecting foods in refrigerator with supervised transformer network".NEUROCOMPUTING 379(2020):162-171.
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