Knowledge Commons of Institute of Automation,CAS
FoodDet: Detecting Foods in Refrigerator with Supervised Transformer Network | |
Zhu YS(朱优松)![]() ![]() ![]() ![]() ![]() | |
发表期刊 | Neurocomputing
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2020 | |
卷号 | 379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. |
收录类别 | SCI |
七大方向——子方向分类 | 目标检测、跟踪与识别 |
国重实验室规划方向分类 | 视觉信息处理 |
是否有论文关联数据集需要存交 | 是 |
文献类型 | 期刊论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/51513 |
专题 | 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 |
作者单位 | 中国科学院自动化研究所 |
第一作者单位 | 中国科学院自动化研究所 |
推荐引用方式 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. |
条目包含的文件 | 下载所有文件 | |||||
文件名称/大小 | 文献类型 | 版本类型 | 开放类型 | 使用许可 | ||
fooddet.pdf(2835KB) | 期刊论文 | 作者接受稿 | 开放获取 | CC BY-NC-SA | 浏览 下载 |
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