Design and Implementation of an Embedded Real-Time System for Guiding Visually Impaired Individuals

Computer vision aims to provide computers with vision capabilities similar to humans. Humans use their eyes and their brains to see and understand the world and objects around them. For visually impaired individuals, these capabilities are lost or damaged in different degrees. Their eyes cannot discharge vision responsibilities. This paper aims to design and implement a portable system to help visually impaired individuals in perceiving objects and people around them and estimating their distance precisely. The proposed system uses a CNN-based real-time object detection technique called YOLO (You Look Only Once) with a single camera mounted on Raspberry Pi board. The system also estimates the distance of the detected objects and deliver these data to visually impaired person in audible form. The results show that the system can detect a person and predict his/her distance with 98.8% accuracy.

[1]  Christoph M. Friedrich,et al.  Object Detection Featuring 3D Audio Localization for Microsoft HoloLens - A Deep Learning based Sensor Substitution Approach for the Blind , 2018, HEALTHINF.

[2]  Lamya Albraheem,et al.  Third Eye: An Eye for the Blind to Identify Objects Using Human-Powered Technology , 2015, 2015 International Conference on Cloud Computing (ICCC).

[3]  Andrei Bursuc,et al.  A Smartphone-Based Obstacle Detection and Classification System for Assisting Visually Impaired People , 2013, 2013 IEEE International Conference on Computer Vision Workshops.

[4]  Tetsuo Ono,et al.  CyARM: an alternative aid device for blind persons , 2005, CHI Extended Abstracts.

[5]  Ali Farhadi,et al.  You Only Look Once: Unified, Real-Time Object Detection , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[6]  Peter B. L. Meijer,et al.  An experimental system for auditory image representations , 1992, IEEE Transactions on Biomedical Engineering.

[7]  Jitendra Malik,et al.  Region-Based Convolutional Networks for Accurate Object Detection and Segmentation , 2016, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[8]  Mohammed Bennamoun,et al.  A Guide to Convolutional Neural Networks for Computer Vision , 2018, A Guide to Convolutional Neural Networks for Computer Vision.

[9]  Kaiming He,et al.  Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks , 2015, IEEE Transactions on Pattern Analysis and Machine Intelligence.