Technology of Graphic & Image
|
App. Res. of Comp. Printed Article
Vol. 36, 2019 No. 10
3152-3156

Item recognition based on Faster R-CNN in service robot

Shi Jie
Zhou Yali
Zhang Qizhi
School of Automation, Beijing Information Science & Technology University, Beijing 100192, China
DOI: 10.19734/j.issn.1001-3695.2018.03.0311

Abstract

Traditional commodity recognition processes generally use the more classic image recognition and machine learning algorithms such as support vector machines(SVM), random forest or AdaBoost, then use the basic characteristics of the gradient, texture or color of the target image to recognize commodities. It can be applied in a relatively simple background, but it is hard to have a more prominent performance in a complicated background environment, and it is difficult to achieve a high accuracy. At present, the convolution neural network(CNN), which is superior in target recognition, has become the first choice in many target recognition scenarios. Considering the hardware configuration cost of service robot, Faster R-CNN, a fast algorithm of region-based convolutional neural network(R-CNN), was introduced into the system and identified by CPU. The CNN network was used to extract image features and access to a regional proposal layer behind it. The experimental results show that it is feasible to apply the deep learning recognition method to the service robot platform. The recognition effect is accurate and the test results are good.

Foundation Support

国家自然科学基金资助项目(11672044,11172047)
北京信息科技大学教改项目(2016JGYB09)
2018北京信息科技大学研究生科技创新项目

Publish Information

DOI: 10.19734/j.issn.1001-3695.2018.03.0311
Publish at: Application Research of Computers Printed Article, Vol. 36, 2019 No. 10
Section: Technology of Graphic & Image
Pages: 3152-3156
Serial Number: 1001-3695(2019)10-061-3152-05

Publish History

[2019-10-05] Printed Article

Cite This Article

石杰, 周亚丽, 张奇志. 基于Faster R-CNN的服务机器人物品识别研究 [J]. 计算机应用研究, 2019, 36 (10): 3152-3156. (Shi Jie, Zhou Yali, Zhang Qizhi. Item recognition based on Faster R-CNN in service robot [J]. Application Research of Computers, 2019, 36 (10): 3152-3156. )

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)