Research on the Robot’s Intelligent Inspection, Its Target Detection Method

Lu Jianhong; Tuyatsetseg Badarch

doi:doi:10.11648/j.ajcst.20220502.19

| Peer-Reviewed

Research on the Robot’s Intelligent Inspection, Its Target Detection Method

Lu Jianhong, Tuyatsetseg Badarch

Published in American Journal of Computer Science and Technology (Volume 5, Issue 2)

Received: 5 April 2022 Accepted: 6 May 2022 Published: 24 May 2022

Views: Downloads:

Download PDF

Share This Article

Twitter
Linked In
Facebook

Abstract

Intelligent inspection robot has the characteristics of programmable, and can be applied to various inspection environments. In general, studies show the present trend is to replace humans with an inspection robot thereby reducing the risks and improving the inspection efficiency. The intelligent inspection robot is based on intelligent technology and has programmability. In this paper, based on the research of intelligent inspection robot technology, we analyze inspection techniques, their algorithms, functions, characteristics and other important parameters. The research mainly focuses on two things: the target detection, methods and improved Adaboost algorithm to improve the accuracy of target detection; the Camshift algorithm which is improved to complete tracking design, timely data acquisition, timely problem discovery and timely solution. The target detection and target tracking are studied and their algorithms are analyzed. We present that a tracking algorithm based on improved Camshift deals with the problems which exist in traditional Camshift algorithm. In addition, we present Meanshift algorithm improves the Camshift algorithm for the whole-process tracking, automation and intelligence level, and efficient tracking. Next, combined with relevant technologies and techniques, the algorithm is improved to complete the target detection design and tracking design, and to solve the problems of inaccurate target detection and untimely detection.

Published in	American Journal of Computer Science and Technology (Volume 5, Issue 2) This article belongs to the Special Issue Advances in Computer Science and Future Technology
DOI	10.11648/j.ajcst.20220502.19
Page(s)	88-95
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2022. Published by Science Publishing Group

Keywords

Intelligent Inspection Robot, Target Detection, Improved Adaboost Algorithm, Camshift Algorithm

References

[1]	Dunlap JH, Van Name JM, Henkener JA. Robotic maintenance of overhead transmission lines. IEEE Trans Power Delivery. 1986; 1 (3): 280–284. doi: 10.1109/TPWRD.1986.4308004.
[2]	Huang S., Wu Z. S., Ren Z. G., et al. Review of intelligent inspection robot for electric power. Electrical Measurement & Instrumentation. Vol. 57, 2020; No. 727 (02), pp. 31-43.
[3]	Miao J., You Z. P., Yuan Q. K., et al. Research status and development of intelligent inspection robot. 2021 (2019-24), pp. 99-100.
[4]	Bao Y. G., Huang Z. B., Ji X. H. Research and application of intelligent inspection robot in hydropower plant based on strong artificial intelligence. Hydropower and Pumped Storage. Vol. 5, 2019; No. 23 (01), pp. 51-55+104.
[5]	Bao Y. G., Huang Z. B., Ji X. H. Research and application of intelligent inspection robot in hydropower plant based on strong artificial intelligence. Hydropower automation and Dam Monitoring, 2019, 005 (001), pp. 39-43+92.
[6]	Wei Z. X. Research and development of intelligent inspection robot for railway traction substation. HFUT (Hefei University of Technology), 2019.
[7]	Lv X. W. Research on suspension track intelligent inspection robot. Zhejiang University, 2019.
[8]	Lian J. H., Sun K., Zhu W., et al. Research on poultry health management System based on intelligent inspection robot. Shandong Agricultural Sciences, Vol. 51. 2019, No. 335 (07), pp. 158-161.
[9]	Liu D. T., Jiang Y. J., Mao Y., et al. Research on application of intelligent inspection robot in power distribution room. Automation & Instrumentation. 2020, No. 247 (05), pp. 184-186+190.
[10]	Peng G. L., Peng D. G., Zhang H., et al. Research on patch-head control of inspection robot based on Faster R-CNN and Fuzzy PID. Microelectronics & Computer. 2019, 036 (003), pp. 61-65.
[11]	Wang X. M., Zhao J. P., Lu Q. C., et al. Research on wind resistance performance of intelligent inspection robot in substation. Information Technology. 2019 (11).
[12]	Li J., Yang F.. (2018). Research on multi-robot scheduling algorithms based on machine vision. EURASIP J. Image Video Process. 2018, 1–11. 10.1186/s13640-018-0355-x.
[13]	Zhengjie Wang, Yuan Zhao, Jifen Zhang, Yinjing Guo, Research on Motion Detection of Video Surveillance System, 3rd International Congress on Image and Signal Processing, vol. 1, pp. 193-197, October 2010.
[14]	K. Suganya Devi, N. Malmurugan, M. Manikandan, Object Motion Detection in Video Frames Using Background Frame Matching, International Journal of Computer Trends and Technology, Vol. 4, Issue 6, pp 1928-1931, June 2013.
[15]	Wu X., D. Li, Liu T., et al. Research on Anti-fall Algorithms of Substation Intelligent Inspection Robot. 2020.
[16]	Liu J, Zhou X, Yizhen X U, et al. Design of intelligent inspection robot system for operation and maintenance of substation equipment. Automation & Instrumentation. 2019.
[17]	Chengru L. I., Mao F. U. Research on Establishment of Expert Database for Maintenance Strategy of Intelligent Inspection Robot System. Electric Engineering. 2019.
[18]	Teng Y., Chen S., Deng J., et al. Application of Intelligent Inspection Robot System in Sutong GIL Utility Tunnel Project. Gaodianya Jishu/high Voltage Engineering. 2019, 45 (2), pp. 393-401.
[19]	Wang C., Yin L., Zhao Q., et al. An intelligent robot for indoor substation inspection. Industrial Robot. 2020, ahead-of-print.
[20]	Bui H. D., Nguyen S., Billah U. H., et al. Control Framework for a Hybrid-steel Bridge Inspection Robot. // 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems - IROS2020. IEEE, 2020.
[21]	Jiacheng Z, Mingjin F, Zexu W, et al. 2019 Structure Design of Intelligent Inspection Robot for Electric Cabinet. Mechanical Engineer.
[22]	H. C. Viswanath and R. A. Jones, "Multiple Target Detection Using Optical Flow," Proceedings of the 1988 IEEE International Conference on Systems, Man, and Cybernetics, 1988, pp. 1002-1005, doi: 10.1109/ICSMC.1988.712860.
[23]	Zhang K, Zhang L and Yang M-H. Fast compressive tracking. IEEE Trans Pattern Anal Mach Intell 2014; 36: 2002–2015.
[24]	Wang Xu. Target detection system based on Adaboost algorithm. Xi'an: Xi'an University of Electronic Science and Technology. 2012; 72: 162-169.
[25]	Yan, Z., Liang, W. and lv, H. (2014) A Target Tracking Algorithm Based on Improved Camshift and UKF. Journal of Software Engineering and Applications, 7, 1065-1073. doi: 10.4236/jsea.2014.713094.
[26]	Wu, H. M. and Zheng, X. S. (2009) Efficient and Improved Camshift Tracking Algorithm. Computer Engineering and Application, 45, 178-180.

Cite This Article

Plain Text BibTeX RIS

APA Style

Lu Jianhong, Tuyatsetseg Badarch. (2022). Research on the Robot’s Intelligent Inspection, Its Target Detection Method. American Journal of Computer Science and Technology, 5(2), 88-95. https://doi.org/10.11648/j.ajcst.20220502.19

Copy | Download

ACS Style

Lu Jianhong; Tuyatsetseg Badarch. Research on the Robot’s Intelligent Inspection, Its Target Detection Method. Am. J. Comput. Sci. Technol. 2022, 5(2), 88-95. doi: 10.11648/j.ajcst.20220502.19

Copy | Download

AMA Style

Lu Jianhong, Tuyatsetseg Badarch. Research on the Robot’s Intelligent Inspection, Its Target Detection Method. Am J Comput Sci Technol. 2022;5(2):88-95. doi: 10.11648/j.ajcst.20220502.19

Copy | Download

@article{10.11648/j.ajcst.20220502.19,
  author = {Lu Jianhong and Tuyatsetseg Badarch},
  title = {Research on the Robot’s Intelligent Inspection, Its Target Detection Method},
  journal = {American Journal of Computer Science and Technology},
  volume = {5},
  number = {2},
  pages = {88-95},
  doi = {10.11648/j.ajcst.20220502.19},
  url = {https://doi.org/10.11648/j.ajcst.20220502.19},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajcst.20220502.19},
  abstract = {Intelligent inspection robot has the characteristics of programmable, and can be applied to various inspection environments. In general, studies show the present trend is to replace humans with an inspection robot thereby reducing the risks and improving the inspection efficiency. The intelligent inspection robot is based on intelligent technology and has programmability. In this paper, based on the research of intelligent inspection robot technology, we analyze inspection techniques, their algorithms, functions, characteristics and other important parameters. The research mainly focuses on two things: the target detection, methods and improved Adaboost algorithm to improve the accuracy of target detection; the Camshift algorithm which is improved to complete tracking design, timely data acquisition, timely problem discovery and timely solution. The target detection and target tracking are studied and their algorithms are analyzed. We present that a tracking algorithm based on improved Camshift deals with the problems which exist in traditional Camshift algorithm. In addition, we present Meanshift algorithm improves the Camshift algorithm for the whole-process tracking, automation and intelligence level, and efficient tracking. Next, combined with relevant technologies and techniques, the algorithm is improved to complete the target detection design and tracking design, and to solve the problems of inaccurate target detection and untimely detection.},
 year = {2022}
}

Copy | Download

TY  - JOUR
T1  - Research on the Robot’s Intelligent Inspection, Its Target Detection Method
AU  - Lu Jianhong
AU  - Tuyatsetseg Badarch
Y1  - 2022/05/24
PY  - 2022
N1  - https://doi.org/10.11648/j.ajcst.20220502.19
DO  - 10.11648/j.ajcst.20220502.19
T2  - American Journal of Computer Science and Technology
JF  - American Journal of Computer Science and Technology
JO  - American Journal of Computer Science and Technology
SP  - 88
EP  - 95
PB  - Science Publishing Group
SN  - 2640-012X
UR  - https://doi.org/10.11648/j.ajcst.20220502.19
AB  - Intelligent inspection robot has the characteristics of programmable, and can be applied to various inspection environments. In general, studies show the present trend is to replace humans with an inspection robot thereby reducing the risks and improving the inspection efficiency. The intelligent inspection robot is based on intelligent technology and has programmability. In this paper, based on the research of intelligent inspection robot technology, we analyze inspection techniques, their algorithms, functions, characteristics and other important parameters. The research mainly focuses on two things: the target detection, methods and improved Adaboost algorithm to improve the accuracy of target detection; the Camshift algorithm which is improved to complete tracking design, timely data acquisition, timely problem discovery and timely solution. The target detection and target tracking are studied and their algorithms are analyzed. We present that a tracking algorithm based on improved Camshift deals with the problems which exist in traditional Camshift algorithm. In addition, we present Meanshift algorithm improves the Camshift algorithm for the whole-process tracking, automation and intelligence level, and efficient tracking. Next, combined with relevant technologies and techniques, the algorithm is improved to complete the target detection design and tracking design, and to solve the problems of inaccurate target detection and untimely detection.
VL  - 5
IS  - 2
ER  -

Copy | Download

Author Information

Lu Jianhong

School of Information Technology and Design, Mongolian National University, Ulaanbaatar, Mongolia
Tuyatsetseg Badarch

Civil Engineering Department, Shandong Jiaotong University, Jinan City, China

Download PDF

Sections

Plain Text BibTeX RIS

APA Style

Lu Jianhong, Tuyatsetseg Badarch. (2022). Research on the Robot’s Intelligent Inspection, Its Target Detection Method. American Journal of Computer Science and Technology, 5(2), 88-95. https://doi.org/10.11648/j.ajcst.20220502.19

Copy | Download

ACS Style

Lu Jianhong; Tuyatsetseg Badarch. Research on the Robot’s Intelligent Inspection, Its Target Detection Method. Am. J. Comput. Sci. Technol. 2022, 5(2), 88-95. doi: 10.11648/j.ajcst.20220502.19

Copy | Download

AMA Style

Lu Jianhong, Tuyatsetseg Badarch. Research on the Robot’s Intelligent Inspection, Its Target Detection Method. Am J Comput Sci Technol. 2022;5(2):88-95. doi: 10.11648/j.ajcst.20220502.19

Copy | Download

@article{10.11648/j.ajcst.20220502.19,
  author = {Lu Jianhong and Tuyatsetseg Badarch},
  title = {Research on the Robot’s Intelligent Inspection, Its Target Detection Method},
  journal = {American Journal of Computer Science and Technology},
  volume = {5},
  number = {2},
  pages = {88-95},
  doi = {10.11648/j.ajcst.20220502.19},
  url = {https://doi.org/10.11648/j.ajcst.20220502.19},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajcst.20220502.19},
  abstract = {Intelligent inspection robot has the characteristics of programmable, and can be applied to various inspection environments. In general, studies show the present trend is to replace humans with an inspection robot thereby reducing the risks and improving the inspection efficiency. The intelligent inspection robot is based on intelligent technology and has programmability. In this paper, based on the research of intelligent inspection robot technology, we analyze inspection techniques, their algorithms, functions, characteristics and other important parameters. The research mainly focuses on two things: the target detection, methods and improved Adaboost algorithm to improve the accuracy of target detection; the Camshift algorithm which is improved to complete tracking design, timely data acquisition, timely problem discovery and timely solution. The target detection and target tracking are studied and their algorithms are analyzed. We present that a tracking algorithm based on improved Camshift deals with the problems which exist in traditional Camshift algorithm. In addition, we present Meanshift algorithm improves the Camshift algorithm for the whole-process tracking, automation and intelligence level, and efficient tracking. Next, combined with relevant technologies and techniques, the algorithm is improved to complete the target detection design and tracking design, and to solve the problems of inaccurate target detection and untimely detection.},
 year = {2022}
}

Copy | Download

TY  - JOUR
T1  - Research on the Robot’s Intelligent Inspection, Its Target Detection Method
AU  - Lu Jianhong
AU  - Tuyatsetseg Badarch
Y1  - 2022/05/24
PY  - 2022
N1  - https://doi.org/10.11648/j.ajcst.20220502.19
DO  - 10.11648/j.ajcst.20220502.19
T2  - American Journal of Computer Science and Technology
JF  - American Journal of Computer Science and Technology
JO  - American Journal of Computer Science and Technology
SP  - 88
EP  - 95
PB  - Science Publishing Group
SN  - 2640-012X
UR  - https://doi.org/10.11648/j.ajcst.20220502.19
AB  - Intelligent inspection robot has the characteristics of programmable, and can be applied to various inspection environments. In general, studies show the present trend is to replace humans with an inspection robot thereby reducing the risks and improving the inspection efficiency. The intelligent inspection robot is based on intelligent technology and has programmability. In this paper, based on the research of intelligent inspection robot technology, we analyze inspection techniques, their algorithms, functions, characteristics and other important parameters. The research mainly focuses on two things: the target detection, methods and improved Adaboost algorithm to improve the accuracy of target detection; the Camshift algorithm which is improved to complete tracking design, timely data acquisition, timely problem discovery and timely solution. The target detection and target tracking are studied and their algorithms are analyzed. We present that a tracking algorithm based on improved Camshift deals with the problems which exist in traditional Camshift algorithm. In addition, we present Meanshift algorithm improves the Camshift algorithm for the whole-process tracking, automation and intelligence level, and efficient tracking. Next, combined with relevant technologies and techniques, the algorithm is improved to complete the target detection design and tracking design, and to solve the problems of inaccurate target detection and untimely detection.
VL  - 5
IS  - 2
ER  -

Copy | Download