Open Access

ARTICLE

Few-Shot Object Detection Based on the Transformer and High-Resolution Network

by Dengyong Zhang^1,2, Huaijian Pu^1,2, Feng Li^1,2,*, Xiangling Ding³, Victor S. Sheng⁴

1 Hunan Provincial Key Laboratory of Intelligent Processing of Big Data on Transportation, Changsha University of Science and Technology, Changsha, 410114, China
2 School of Computer and Communication Engineering, Changsha University of Science and Technology, Changsha, 410114, China
3 School of Computer Science and Engineering, Hunan University of Science and Technology, Xiangtan, 411004, China
4 Department of Computer Science, Texas Tech University, Lubbock, 79409, TX, USA

* Corresponding Author: Feng Li. Email:

Computers, Materials & Continua 2023, 74(2), 3439-3454. https://doi.org/10.32604/cmc.2023.027267

Received 14 January 2022; Accepted 17 May 2022; Issue published 31 October 2022

Abstract

Now object detection based on deep learning tries different strategies. It uses fewer data training networks to achieve the effect of large dataset training. However, the existing methods usually do not achieve the balance between network parameters and training data. It makes the information provided by a small amount of picture data insufficient to optimize model parameters, resulting in unsatisfactory detection results. To improve the accuracy of few shot object detection, this paper proposes a network based on the transformer and high-resolution feature extraction (THR). High-resolution feature extraction maintains the resolution representation of the image. Channels and spatial attention are used to make the network focus on features that are more useful to the object. In addition, the recently popular transformer is used to fuse the features of the existing object. This compensates for the previous network failure by making full use of existing object features. Experiments on the Pascal VOC and MS-COCO datasets prove that the THR network has achieved better results than previous mainstream few shot object detection.

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Keywords

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