Technology of Graphic & Image
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App. Res. of Comp. Printed Article
Vol. 40, 2023 No. 5
1565-1570

Dual channel spatial interdependent and asymmetric weight-sharing convolution for object detection

Wang Huiru
Wang Chuanxu
Liu Hao
Zhang Hao
College of Information Science & Technology, Qingdao University of Science & Technology, Qingdao Shandong 266061, China
DOI: 10.19734/j.issn.1001-3695.2022.09.0464

Abstract

In the past object detection tasks, many studies construct feature optimization algorithms through spatial and channel information. How to utilize the information distribution of the largest and average features in the channel and space for feature extraction is still a challenge. In response to the above challenges, this paper constructed a dual channel space interdependent network to extract information-dependent features in space and channels. The key was to obtain the vital information distribution on the maximum feature and the average feature. In order to optimize the detection accuracy and computational efficiency of the network, this paper constructed an asymmetric weight-sharing convolution network, using the same asymmetric convolution kernel for joint training to realize weight sharing, which greatly enhanced the robustness of the convolutional kernel parameters. This paper conducted experiments on PASCAL VOC and MS COCO datasets using YOLOv4, YOLOv5, and EfficientDet as detection baselines to verify the effectiveness of the dual channel space interdependent network and the asymmetric weight-sharing convolution network. The highest accuracy increases by 1.98% and 2.6% on the two datasets.

Foundation Support

国家基金面上项目(61672305)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2022.09.0464
Publish at: Application Research of Computers Printed Article, Vol. 40, 2023 No. 5
Section: Technology of Graphic & Image
Pages: 1565-1570
Serial Number: 1001-3695(2023)05-044-1565-06

Publish History

[2022-11-17] Accepted Paper
[2023-05-05] Printed Article

Cite This Article

王慧薷, 王传旭, 刘豪, 等. 基于双分支通道空间依赖和非对称权重共享卷积的目标检测优化结构 [J]. 计算机应用研究, 2023, 40 (5): 1565-1570. (Wang Huiru, Wang Chuanxu, Liu Hao, et al. Dual channel spatial interdependent and asymmetric weight-sharing convolution for object detection [J]. Application Research of Computers, 2023, 40 (5): 1565-1570. )

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  • Application Research of Computers Monthly Journal
  • Journal ID ISSN 1001-3695
    CN  51-1196/TP

Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

Aiming at the urgently needed cutting-edge technology in this discipline, Application Research of Computers reflects the mainstream technology, hot technology and the latest development trend of computer application research at home and abroad in a timely manner. The main contents of the journal include high-level academic papers in this discipline, the latest scientific research results and major application results. The contents of the columns involve new theories of computer discipline, basic computer theory, algorithm theory research, algorithm design and analysis, blockchain technology, system software and software engineering technology, pattern recognition and artificial intelligence, architecture, advanced computing, parallel processing, database technology, computer network and communication technology, information security technology, computer image graphics and its latest hot application technology.

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