Food det: Detecting foods in refrigerator with supervised transformer network
文献类型：期刊论文
| 作者 | Zhu, Yousong1,2 ; Zhao, Xu1,2; Zhao, Chaoyang1,2; Wang, Jinqiao1,2; Lu, Hanqing1,2
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| 刊名 | NEUROCOMPUTING
 |
| 出版日期 | 2020-02-28 |
| 卷号 | 379页码:162-171 |
| 关键词 | Food detection Spatial transformer Object detection |
| ISSN号 | 0925-2312 |
| DOI | 10.1016/j.neucom.2019.10.106 |
| 通讯作者 | Zhu, Yousong(yousong.zhu@nlpr.ia.ac.cn) |
| 英文摘要 | 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. |
| WOS关键词 | CONTEXT |
| 资助项目 | 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 |
| 语种 | 英语 |
| WOS记录号 | WOS:000507464700014 |
| 出版者 | ELSEVIER |
| 资助机构 | Natural Science Foundation of China |
| 源URL | [http://ir.ia.ac.cn/handle/173211/29487]  |
| 专题 | 自动化研究所_模式识别国家重点实验室_图像与视频分析团队 |
| 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 |
| 推荐引用方式 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. |
入库方式： OAI收割
来源：自动化研究所
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