Navigation and Object Detection for Blind Persons Based on Neural Network

Abstract

Tools for blind people with mobility activities in pedestrian pathways have been widely launched, approved and patented. However, there are still shortcomings that can be done only for pedestrian paths or nearby destinations. In this study, both a camera (detection of the pedestrian path) and LiDAR (detection of surrounding objects) sensors to help disability activities. The first stage of image data from the preparatory camera from RGB to XYZ, color filters, close morphology, resizing, learning and testing of neural networks. Bring up 3 voice attitudes information. Attitudes are perpendicular, left tilted, right tilted, or not reversed to the pedestrian yellow path. The second stage of the LiDAR distance points data is processed into 2D array geometry, learning, and testing of neural networks. Bring up the information 8 voice attitudes. Detection of the cycle and distance of objects right side, front, left, right-front, right-left, front-left, right-front-left, not captured. The test results approximately at lux <15000 got 89.7% accuracy for pedestrian path detection and 87.5% for object detection.