In accordance with regulations and requirements, the editorial department's website domain has been changed to arocmag.cn. The original domain (arocmag.com) will be discontinued after Dec. 31st, 2024.
See More
Technology of Graphic & Image
|
App. Res. of Comp. Printed Article
Vol. 38, 2021 No. 4
1212-1215

Real time object detection method based on improved attention transfer

Zhang Chia,b
Liu Hongzhea,b
a. Beijing Key Laboratory of Information Service Engineering, b. College of Robotics, Beijing Union University, Beijing 100101, China
DOI: 10.19734/j.issn.1001-3695.2020.02.0079

Abstract

Recently, deep neural networks need to be deployed with low memory and computing resources, so it is necessary to design an efficient and compact network structure. This paper proposed a model compression method(KE) based on improved attention transfer for the design of compact neural networks, which mainly used a wide residual teacher network(WRN) to guide a compact student network(KENet) by extracting both spatial and channel-wise attention to improve the performance, and applied this method to real-time object detection. The image classification experiment on CIFAR verifies that the knowledge distillation method with improved attention transfer can improve the performance of the compact model. The object detection experiment on VOC verifies that the model KEDet has good accuracy(72.7 mAP) and time performance(86 fps). The experimental results show that the object detection model based on improved attention transfer has good accuracy and real-time performance.

Foundation Support

国家自然科学基金资助项目(61871039,61906017,61802019)
北京市教委项目(KM202111417001,KM201911417001)
视觉智能协同创新中心项目(CYXC2011)
北京联合大学学术研究项目(ZB10202003,ZK80202001,XP202015,BPHR2019AZ01)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2020.02.0079
Publish at: Application Research of Computers Printed Article, Vol. 38, 2021 No. 4
Section: Technology of Graphic & Image
Pages: 1212-1215
Serial Number: 1001-3695(2021)04-048-1212-04

Publish History

[2021-04-05] Printed Article

Cite This Article

张弛, 刘宏哲. 基于改进注意力迁移的实时目标检测方法 [J]. 计算机应用研究, 2021, 38 (4): 1212-1215. (Zhang Chi, Liu Hongzhe. Real time object detection method based on improved attention transfer [J]. Application Research of Computers, 2021, 38 (4): 1212-1215. )

About the Journal

  • 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.

Application Research of Computers has many high-level readers and authors, and its readers are mainly senior and middle-level researchers and engineers engaged in the field of computer science, as well as teachers and students majoring in computer science and related majors in colleges and universities. Over the years, the total citation frequency and Web download rate of Application Research of Computers have been ranked among the top of similar academic journals in this discipline, and the academic papers published are highly popular among the readers for their novelty, academics, foresight, orientation and practicality.

Indexed & Evaluation

  • The Second National Periodical Award 100 Key Journals
  • Double Effect Journal of China Journal Formation
  • the Core Journal of China (Peking University 2023 Edition)
  • the Core Journal for Science
  • Chinese Science Citation Database (CSCD) Source Journals
  • RCCSE Chinese Core Academic Journals
  • Journal of China Computer Federation
  • 2020-2022 The World Journal Clout Index (WJCI) Report of Scientific and Technological Periodicals
  • Full-text Source Journal of China Science and Technology Periodicals Database
  • Source Journal of China Academic Journals Comprehensive Evaluation Database
  • Source Journals of China Academic Journals (CD-ROM Version), China Journal Network
  • 2017-2019 China Outstanding Academic Journals with International Influence (Natural Science and Engineering Technology)
  • Source Journal of Top Academic Papers (F5000) Program of China's Excellent Science and Technology Journals
  • Source Journal of China Engineering Technology Electronic Information Network and Electronic Technology Literature Database
  • Source Journal of British Science Digest (INSPEC)
  • Japan Science and Technology Agency (JST) Source Journal
  • Russian Journal of Abstracts (AJ, VINITI) Source Journals
  • Full-text Journal of EBSCO, USA
  • Cambridge Scientific Abstracts (Natural Sciences) (CSA(NS)) core journals
  • Poland Copernicus Index (IC)
  • Ulrichsweb (USA)