CASIA OpenIR  > 紫东太初大模型研究中心  > 图像与视频分析
FoodDet: Detecting Foods in Refrigerator with Supervised Transformer Network
Zhu YS(朱优松); Zhao X(赵旭); Zhao CY(赵朝阳); Wang JQ(王金桥); Lu HQ(卢汉清)
Source PublicationNeurocomputing
2020
Volume379Pages:162-171
Abstract

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.

Indexed BySCI
Sub direction classification目标检测、跟踪与识别
planning direction of the national heavy laboratory视觉信息处理
Paper associated data
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/51513
Collection紫东太初大模型研究中心_图像与视频分析
紫东太初大模型研究中心
Affiliation中国科学院自动化研究所
First Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Recommended Citation
GB/T 7714
Zhu YS,Zhao X,Zhao CY,et al. FoodDet: Detecting Foods in Refrigerator with Supervised Transformer Network[J]. Neurocomputing,2020,379:162-171.
APA Zhu YS,Zhao X,Zhao CY,Wang JQ,&Lu HQ.(2020).FoodDet: Detecting Foods in Refrigerator with Supervised Transformer Network.Neurocomputing,379,162-171.
MLA Zhu YS,et al."FoodDet: Detecting Foods in Refrigerator with Supervised Transformer Network".Neurocomputing 379(2020):162-171.
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