[go: up one dir, main page]
Skip to main content
Springer Nature Link
Log in

A computer vision and artificial intelligence based cost-effective object sensing robot

  • Regular Paper
  • Published:
International Journal of Intelligent Robotics and Applications Aims and scope Submit manuscript

Abstract

In this research, we intend to present an enhanced object detection system incorporating a few well-known computer vision techniques, machine learning algorithms, as well as smart sensors in a more organized way. In essence, a computer vision based system in cooperation with machine learning approach has been employed to detect objects in parallel with a hardware-based ultrasonic sensor unit. The units can not only operate independently but also can cooperate in terms of detection result and hence, this type of approach can be termed as a cooperative object sensing system. Moreover, another key point is the prototype can determine free path to ensure smooth traversing which makes it effective in case of unforeseen scenario. By the same token, this prototype is also able to detect the type of object which is another compelling evidence of its novelty. With this intention in mind, we conducted a field test and evaluated its performance which justified the features specified above.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

References

  • Ahmed, S., Saifuddin, K., Ahmed, A., Hossain, A., Iqbal, M.: Identification and volume estimation of dental caries using CT image. In: 2017 IEEE International Conference on Telecommunications and Photonics (ICTP), Dhaka, Bangladesh (2017)

  • Bairagi, A., Ahmed, A., Hassan, A.: Enhanced edge detection by feed-forward and back-propagation neural network. In: International Conference on New Trends in Engineering & Technology (ICNTET-2018), Tiruvallur, India (2018, in press)

  • Dollar, P., Wojek, C., Schiele, B. and Perona, P.: Pedestrian detection: a benchmark. In: 2009 IEEE Conference on Computer Vision and Pattern Recognition, Miami, FL, USA (2009)

  • Dollar, P., Wojek, C., Schiele, B., Perona, P.: Pedestrian detection: an evaluation of the state of the art. IEEE Trans. Pattern Anal. Mach. Intell. 34(4), 743–761 (2012)

    Article  Google Scholar 

  • Dollar, P., Appel, R., Belongie, S., Perona, P.: Fast feature pyramids for object detection. IEEE Trans. Pattern Anal. Mach. Intell. 36(8), 1532–1545 (2014)

    Article  Google Scholar 

  • Gao, W., Zhang, X., Yang, L., Liu, H.: An improved Sobel edge detection. In: 2010 3rd International Conference on Computer Science and Information Technology (2010)

  • Girshick, R., Donahue, J., Darrell, T., Malik, J.: Region-based convolutional networks for accurate object detection and segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 38(1), 142–158 (2016)

    Article  Google Scholar 

  • Guo, H., Shen, C., Zhang, H., Chen, H., Jia, R.: Simultaneous trajectory planning and tracking using an MPC method for cyber-physical systems: a case study of obstacle avoidance for an intelligent vehicle. IEEE Trans. Ind. Inf. 14(9), 4273–4283 (2018)

    Article  Google Scholar 

  • Hamledari, H., McCabe, B., Davari, S.: Automated computer vision-based detection of components of under-construction indoor partitions. Autom. Constr. 74, 78–94 (2017)

    Article  Google Scholar 

  • Hu, Y., Gu, W., Zhang, H. and Chen, H.: Adaptive robust triple-step control for compensating cogging torque and model uncertainty in a DC motor. In: IEEE Transactions Systems, Man and Cybernetics Systems, pp. 1–10 (2018). https://doi.org/10.1109/TSMC.2018.2799581

    Article  Google Scholar 

  • Jiang, K., Zhang, H., Karimi, H., Lin, J. and Song, L.: Simultaneous input and state estimation for integrated motor-transmission systems in a controller area network environment via an adaptive unscented Kalman Filter. In: IEEE Transactions on Systems, Man, and Cybernetics: Systems, pp. 1–10 (2018)

  • Jin, R., Yin, J., Zhou, W., Yang, J.: Improved multiscale edge detection method for polarimetric SAR images. IEEE Geosci. Remote Sens. Lett. 13(8), 1104–1108 (2016)

    Article  Google Scholar 

  • Kleeman, L., Kuc, R.: Sonar sensing. In: Siciliano, B., Khatib, O. (eds.) Springer Handbook of Robotics. Springer, Berlin, Heidelberg (2008)

    Google Scholar 

  • Melin, P., Gonzalez, C., Castro, J., Mendoza, O., Castillo, O.: Edge-detection method for image processing based on generalized type-2 fuzzy logic. IEEE Trans. Fuzzy Syst. 22(6), 1515–1525 (2014)

    Article  Google Scholar 

  • Paul, S., Ahmed, A., Mollah, M., Rayhan, M., Hossain, E.: Simplistic approach to design a prototype of autonomous, affordable, and highly efficient agricultural sprayer robot. In: 2017 4th International Conference on Advances in Electrical Engineering (ICAEE), Dhaka, Bangladesh (2017)

  • Perona, P., Malik, J.: Scale-space and edge detection using anisotropic diffusion. IEEE Trans. Pattern Anal. Mach. Intell. 12(7), 629–639 (1990)

    Article  Google Scholar 

  • Suleiman, A., Sze, V.: An energy-efficient hardware implementation of HOG-based object detection at 1080HD 60 fps with multi-scale support. J. Signal Process. Syst. 84(3), 325–337 (2015)

    Article  Google Scholar 

  • Viola, P., Jones, M.: Rapid object detection using a boosted cascade of simple features. In: Proceedfings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2001, vol. 1, pp. I–I. IEEE, Kauai, HI, USA (2001)

  • Vision.caltech.edu. (2019). Caltech pedestrian detection benchmark. [online] http://www.vision.caltech.edu/Image_Datasets/CaltechPedestrians/ (2019). Accessed 20 Aug 2019

  • Wang, Z., Jia, Y., Huang, H. and Tang, S.: Pedestrian detection using boosted HOG features. In: 2008 11th International IEEE Conference on Intelligent Transportation Systems, Beijing, China (2008)

  • Ying-Dong, Q., Cheng-Song, C., San-Ben, C., Jin-Quan, L.: A fast subpixel edge detection method using Sobel–Zernike moments operator. Image Vis. Comput. 23(1), 11–17 (2005)

    Article  Google Scholar 

  • Zhang, H., Shi, Y., Wang, J., Chen, H.: A new delay-compensation scheme for networked control systems in controller area networks. IEEE Trans. Ind. Electron. 65(9), 7239–7247 (2018). https://doi.org/10.1109/TIE.2018.2795574

    Article  Google Scholar 

Download references

Acknowledgements

We acknowledge the contributions of Shotabdi Roy, Chowdhury Tasnuva Hazera, and Debashish Das. Moreover, we would like to express our gratitude to Abu Shakil Ahmed for his technical lead as well as manuscript editing. Finally, we are thankful to Rumel M.S. Rahman Pir for inspiring us throughout the project.

Author information

Authors and Affiliations

  1. Department of Electrical and Electronic Engineering, Leading University, Sylhet, 3112, Bangladesh

    Shotabdi Roy, Chowdhury Tasnuva Hazera, Debashish Das, Rumel M. S. Rahman Pir & Abu Shakil Ahmed

Authors
  1. Shotabdi Roy
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Chowdhury Tasnuva Hazera
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Debashish Das
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Rumel M. S. Rahman Pir
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Abu Shakil Ahmed
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Abu Shakil Ahmed.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Roy, S., Hazera, C.T., Das, D. et al. A computer vision and artificial intelligence based cost-effective object sensing robot. Int J Intell Robot Appl 3, 457–470 (2019). https://doi.org/10.1007/s41315-019-00107-1

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s41315-019-00107-1

Keywords

Search

Navigation