Intelligent Smart Glass for Visually Impaired Using Deep Learning Machine Vision Techniques and Robot Operating System (ROS)

  • Aswath SureshEmail author
  • Chetan Arora
  • Debrup Laha
  • Dhruv Gaba
  • Siddhant Bhambri
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 751)


The Smart Glass represents potential aid for people who are visually impaired that might lead to improvements in the quality of life. The smart glass is for the people who need to navigate independently and feel socially convenient and secure while they do so. It is based on the simple idea that blind people do not want to stand out while using tools for help. This paper focuses on the significant work done in the field of wearable electronics and the features which comes as add-ons. The Smart glass consists of ultrasonic sensors to detect the object ahead in real-time and feeds the Raspberry for analysis of the object whether it is an obstacle or a person. It can also assist the person on whether the object is closing in very fast and if so, provides a warning through vibrations in the recognized direction. It has an added feature of GSM, which can assist the person to make a call during an emergency situation. The software framework management of the whole system is controlled using Robot Operating System (ROS). It is developed using ROS catkin workspace with necessary packages and nodes. The ROS was loaded on to Raspberry Pi with Ubuntu Mate.


  1. 1.
    Nguyen, T.H., Nguyen, T.H., Le, T.L., Tran, T.T.H., Vuillerme, N., Vuong, T.P.: A wearable assistive device for the blind using tongue-placed electrotactile display: design and verification. In: 2013 International Conference on Control, Automation and Information Sciences (ICCAIS), pp. 42–47. IEEE (2013)Google Scholar
  2. 2.
    The BuzzClip: IMerciv. Wearable Assistive Technology.
  3. 3.
    Dakopoulos, D., Bourbakis, N.G.: Wearable obstacle avoidance electronic travel aids for blind: a survey. IEEE Trans. Syst. Man Cybern. Part C (Appl. Rev.) 40(1), 25–35 (2010)CrossRefGoogle Scholar
  4. 4.
    Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: towards real-time object detection with region proposal networks. In: Advances in Neural Information Processing Systems, pp. 91–99 (2015)Google Scholar
  5. 5.
    Redmon, J., Divvala, S., Girshick, R., Farhadi, A.: You only look once: unified, real-time object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 779–788 (2016)Google Scholar
  6. 6.
    Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C.-Y., Berg, A.C.: Ssd: single shot multibox detector. In: European Conference on Computer Vision, pp. 21–37. Springer, Cham (2016)CrossRefGoogle Scholar
  7. 7.
    Howard, A.G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Andreetto, M., Adam, H.: Mobilenets: efficient convolutional neural networks for mobile vision applications (2017). arXiv:1704.04861
  8. 8.
    Ross, D.A.: Implementing assistive technology on wearable computers. IEEE Intell. Syst. 16(3), 47–53 (2001)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2019

Authors and Affiliations

  • Aswath Suresh
    • 1
    Email author
  • Chetan Arora
    • 1
  • Debrup Laha
    • 1
  • Dhruv Gaba
    • 1
  • Siddhant Bhambri
    • 2
  1. 1.Department of Mechanical and Aerospace EngineeringNew York UniversityNew YorkUSA
  2. 2.Department of Electronics and Communication EngineeringBharati Vidyapeeth’s College of EngineeringPuneIndia

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