Advertisement

Object Classification for Robotic Platforms

  • Samuel Brandenburg
  • Pedro MachadoEmail author
  • Pranjali Shinde
  • João Filipe Ferreira
  • T. M. McGinnity
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1093)

Abstract

Computer vision has been revolutionised in recent years by increased research in convolutional neural networks (CNNs); however, many challenges remain to be addressed in order to ensure fast and accurate image processing when applying these techniques to robotics. These challenges consist of handling extreme changes in scale, illumination, noise, and viewing angles of a moving object. The project main contribution is to provide insight on how to properly train a convolutional neural network (CNN), a specific type of DNN, for object tracking in the context of industrial robotics. The proposed solution aims to use a combination of documented approaches to replicate a pick-and-place task with an industrial robot using computer vision feeding a YOLOv3 CNN. Experimental tests, designed to investigate the requirements of training the CNN in this context, were performed using a variety of objects that differed in shape and size in a controlled environment. The general focus was to detect the objects based on their shape; as a result, a suitable and secure grasp could be selected by the robot. The findings in this article reflect the challenges of training the CNN through brute force. It also highlights the different methods of annotating images and the ensuing results obtained after training the neural network.

Keywords

Object classification Training YOLOv3 CNN ROS 

References

  1. 1.
    Aggarwal, C.C.: Convolutional neural networks. In: Neural Networks and Deep Learning, pp. 315–371. Springer, Cham (2018). http://link.springer.com/10.1007/978-3-319-94463-0_8CrossRefGoogle Scholar
  2. 2.
    Brownlee, J.: Overfitting and Underfitting With Machine Learning Algorithms (2016). https://machinelearningmastery.com/overfitting-and-underfitting-with-machine-learning-algorithms/
  3. 3.
    Das, S.: CNN Architectures: LeNet, AlexNet, VGG, GoogLeNet, ResNet and more... (2017). https://medium.com/@sidereal/cnns-architectures-lenet-alexnet-vgg-googlenet-resnet-and-more-666091488df5
  4. 4.
    Ferreira, J.F., Dias, J.: Attentional mechanisms for socially interactive robots – a survey. IEEE Trans. Auton. Mental Dev. 6(2), 110–123 (2014)CrossRefGoogle Scholar
  5. 5.
    Jafri, R., Aljuhani, A.M., Ali, S.A.: A tangible interface-based application for teaching tactual shape perception and spatial awareness sub-concepts to visually impaired children. Proc. Manuf. 3(Ahfe), 5562–5569 (2015).  https://doi.org/10.1016/j.promfg.2015.07.734CrossRefGoogle Scholar
  6. 6.
    Kragic, D., Gustafson, J., Karaoguz, H., Jensfelt, P., Krug, R.: Interactive, collaborative robots: challenges and opportunities. In: Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence (IJCAI 2018), pp. 18–25 (2018)Google Scholar
  7. 7.
    Kuo, C.C.: Understanding convolutional neural networks with a mathematical model. J. Vis. Commun. Image Representation 41, 406–413 (2016)CrossRefGoogle Scholar
  8. 8.
    Redmon, J., Divvala, S., Girshick, R., Farhadi, A.: You only look once: unified, real-time object detection. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (June 2016).  https://doi.org/10.1109/CVPR.2016.91
  9. 9.
    Redmon, J., Farhadi, A.: YOLO9000: Better, faster, stronger. In: Proceedings - 30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017, January 2017, pp. 6517–6525 (2017)Google Scholar
  10. 10.
    Redmon, J., Farhadi, A.: Yolov3: an incremental improvement (2018)Google Scholar
  11. 11.
    Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: towards real-time object detection with region proposal networks. IEEE Trans. Pattern Anal. Mach. Intell. 39(6), 1137–1149 (2017)CrossRefGoogle Scholar
  12. 12.
    Sharma, P.: A Step-by-Step Introduction to the Basic Object Detection Algorithms (Part 1) (2018). https://www.analyticsvidhya.com/blog/2018/10/a-step-by-step-introduction-to-the-basic-object-detection-algorithms-part-1/
  13. 13.
    Wirth, R.: CRISP-DM: towards a standard process model for data mining. In: Proceedings of the Fourth International Conference on the Practical Application of Knowledge Discovery and Data Mining, pp. 29–39 (2000)Google Scholar
  14. 14.
    Yazdi, M., Bouwmans, T.: New trends on moving object detection in video images captured by a moving camera: a survey. Comput. Sci. Rev. 28, 157–177 (2018).  https://doi.org/10.1016/j.cosrev.2018.03.001MathSciNetCrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Samuel Brandenburg
    • 1
  • Pedro Machado
    • 1
    Email author
  • Pranjali Shinde
    • 2
  • João Filipe Ferreira
    • 1
    • 3
  • T. M. McGinnity
    • 1
    • 4
  1. 1.Computational Neurosciences and Cognitive Robotics GroupNottingham Trent UniversityNottinghamUK
  2. 2.INESC TEC, R. Dr. Roberto FriasPortoPortugal
  3. 3.Institute of Systems and Robotics, University of Coimbra, Polo IICoimbraPortugal
  4. 4.Intelligent Systems Research CentreUlster UniversityLondonderryUK

Personalised recommendations