基于智能语义分割的视障用户动力轮椅实时辅助导航系统

Elhassan Mohamed, K. Sirlantzis, G. Howells
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引用次数: 0

摘要

有运动障碍的人可能会发现,由于他们的合并症,驾驶电动轮椅是一项具有挑战性的任务。某些有行动障碍的视障人士因视力问题而没有获安排电动轮椅。然而,电动轮椅对大多数残疾用户来说是必不可少的,用于通勤和社交。这对他们的独立和幸福至关重要。在本文中,我们建议使用基于深度学习算法的语义分割(SS)系统为视障轮椅使用者提供环境线索和信息,以帮助他们进行导航过程。该系统对室内环境中的物体进行分类,并根据用户的情况在显示屏上显示带有注释的输出。用户可以选择一个目标物体,系统可以显示距离轮椅当前位置的估计距离。该系统使用安装在轮椅上的深度摄像头实时运行,并在轮椅前显示场景,每个像素都用可区分的颜色标注,以代表环境的不同组成部分以及与目标物体的距离。我们的系统已经设计、实现并部署在一个真实的动力轮椅上进行实际评估。在a)半忽略和b)近视眼条件下,系统的相对误差分别为19.8%和18.4%,而非SS系统的相对误差分别为47.8%和5.6%。在我们的实验中,使用医学研究中常用的仪器,将健康参与者置于代表上述视觉障碍的模拟条件下。最后,我们的系统有助于在显示器上显示环境的隐藏区域和盲点,如果没有它,视障用户将无法看到这些区域和盲点。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Real-time Powered Wheelchair Assistive Navigation System Based on Intelligent Semantic Segmentation for Visually Impaired Users
People with movement disabilities may find powered wheelchair driving a challenging task due to their comorbidities. Certain visually impaired persons with mobility disabilities are not prescribed a powered wheelchair because of their sight condition. However, powered wheelchairs are essential to the majority of these disabled users for commuting and social interaction. It is vital for their independence and wellbeing. In this paper, we propose to use a semantic segmentation (SS) system based on deep learning algorithms to provide environmental cues and information to visually impaired wheelchair users to aid with the navigation process. The system classifies the objects of the indoor environment and presents the annotated output on a display customised to the user's condition. The user can select a target object, for which the system can display the estimated distance from the current position of the wheelchair. The system runs in real-time, using a depth camera installed on the wheelchair, and it displays the scene in front of the wheelchair with every pixel annotated with distinguishable colour to represent the different components of the environment along with the distance to the target object. Our system has been designed, implemented and deployed on a real powered wheelchair for practical evaluation. The proposed system helped the users to estimate more accurately the distance to the target objects with a relative error of 19.8% and 18.4% for the conditions of a) semi-neglect and b) short-sightedness, respectively, compared to errors of 47.8% and 5.6% without the SS system. In our experiments, healthy participants were put in simulated conditions representing the above visual impairments using instruments commonly used in medical research for this purpose. Finally, our system helps to visualise, on the display, hidden areas of the environment and blind spots that visually impaired users would not be able to see without it.
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