Intelligent Smart Glass for Visually Impaired Using Deep Learning Machine Vision Techniques and Robot Operating System (ROS)
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.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.The BuzzClip: IMerciv. Wearable Assistive Technology. www.imerciv.com/index.shtml
- 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.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
- 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