Institutional Repository of Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
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 |
ISSN | 0925-2312 |
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 |
DOI | 10.1016/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 | |
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 |
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(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. |
条目包含的文件 | ||||||
文件名称/大小 | 文献类型 | 版本类型 | 开放类型 | 使用许可 | ||
Food det_ Detecting (2790KB) | 期刊论文 | 作者接受稿 | 开放获取 | CC BY-NC-SA | 浏览 |
除非特别说明,本系统中所有内容都受版权保护,并保留所有权利。
修改评论