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
发表期刊NEUROCOMPUTING
ISSN0925-2312
2020-02-28
卷号379期号:28页码:162-171
摘要

Most 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.

关键词Food detection Spatial transformer Object detection
DOI10.1016/j.neucom.2019.10.106
关键词[WOS]CONTEXT
收录类别SCI
语种英语
资助项目Natural Science Foundation of China[61876086] ; Natural Science Foundation of China[61806200] ; Natural Science Foundation of China[61772527] ; Natural Science Foundation of China[61772527] ; Natural Science Foundation of China[61806200] ; Natural Science Foundation of China[61876086]
WOS研究方向Computer Science
WOS类目Computer Science, Artificial Intelligence
WOS记录号WOS:000507464700014
出版者ELSEVIER
七大方向——子方向分类图像视频处理与分析
引用统计
被引频次:3[WOS]   [WOS记录]     [WOS相关记录]
文献类型期刊论文
条目标识符http://ir.ia.ac.cn/handle/173211/29488
专题模式识别国家重点实验室_图像与视频分析
通讯作者Zhu, Yousong
作者单位1.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
第一作者单位模式识别国家重点实验室
通讯作者单位模式识别国家重点实验室
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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(28):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(28),162-171.
MLA Zhu, Yousong,et al."Food det: Detecting foods in refrigerator with supervised transformer network".NEUROCOMPUTING 379.28(2020):162-171.
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