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EPformer: an efficient transformer-based approach for retail product detection in fisheye images

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成果类型:
期刊论文
作者:
Yang, Yunfei;Deng, Hongwei
通讯作者:
Hongwei Deng
作者机构:
[Deng, Hongwei; Yang, Yunfei] Hengyang Normal Univ, Coll Comp Sci & Technol, Hengyang, Peoples R China.
[Deng, Hongwei] Hunan Prov Key Lab Intelligent Informat Proc & App, Hengyang, Peoples R China.
通讯机构:
[Hongwei Deng] H
Hengyang Normal Univ.
语种:
英文
关键词:
Convolutional neural networks;Feature extraction;Product design;Sales;Textures;Fine grained;Fine-grained feature;Fish-eye;Fish-eye cameras;Fisheye deformation;Fisheye images;Object deformation;Product detection;Retail product detection;Visual transformer;Cameras
期刊:
Journal of Electronic Imaging
ISSN:
1017-9909
年:
2023
卷:
32
期:
1
基金类别:
The authors would like to acknowledge the support of the Scientific Research Project of Hunan Provincial Department of Education and the Science and Technology Plan Project of Hunan Province. This work was supported in part by the Scientific Research Project of Hunan Provincial Department of Education (Grant No. 18A332), the Science and Technology Plan Project of Hunan Province (Grant No. 2016TP1020), and the Hunan Provincial Natural Science Foundation of China (Grant No. 2022JJ50016). The authors declare that they have no conflicts of interest to report regarding the present study.
机构署名:
本校为第一机构
院系归属:
计算机科学与技术学院
摘要:
Retail product detection in fisheye camera capture scenes frequently suffers from excessive object occlusion and deformation, as well as difficulty in distinguishing products with small fine-grained differences, so accurately classifying and localizing products in these images presents a challenge for computer vision. We propose an efficient product detection network called EPformer by fusing a visual transformer and convolutional neural network to reliably detect retail products in fisheye images. We employ a shifted window strategy for featur...

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