Survey of Transformer-Based Object Detection Algorithms

被引：10
作者：
Li, Jian [1 ]
Du, Jianqiang [1 ]
Zhu, Yanchen [1 ]
Guo, Yongkun [1 ]
机构：
[1] College of Computer Science, Jiangxi University of Chinese Medicine, Nanchang
来源：
Computer Engineering and Applications | 2023年 / 59卷 / 10期
关键词：
convolutional neural network（CNN）; deep learning; image processing; object detection; Transformer;
D O I：
10.3778/j.issn.1002-8331.2211-0133
中图分类号：
学科分类号：
摘要：
Transformer is a kind of deep learning framework with strong modeling and parallel computing capabilities. At present, object detection algorithm based on Transformer has become a hotspot. In order to further explore new ideas and directions, this paper summarizes the existing object detection algorithm based on Transformer as well as a variety of object detection data sets and their application scenarios. This paper describes the correlation algorithms for Transformer based object detection from four aspects, i.e. feature extraction, object estimation, label matching policy and application of algorithm, compares the Transformer algorithm with the object detection algorithm based on convolutional neural network, analyzes the advantages and disadvantages of Transformer in object detection task, and proposes a general framework for Transformer based object detection model. Finally, the prospect of development trend of Transformer in the field of object detection is put forward. © 2023 Journal of Computer Engineering and Applications Beijing Co., Ltd.; Science Press. All rights reserved.
引用
收藏
页码：48 / 64
页数：16
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