Joshi Rakesh Chandra, Yadav Saumya, Dutta Malay Kishore, Travieso-Gonzalez Carlos M
Centre for Advanced Studies, Dr. A.P.J. Abdul Kalam Technical University, Lucknow 226031, India.
Institute for Technological Development and Innovation in Communications (IDeTIC), University of Las Palmas de Gran Canaria (ULPGC), 35017 Las Palmas de G.C., Spain.
Entropy (Basel). 2020 Aug 27;22(9):941. doi: 10.3390/e22090941.
Visually impaired people face numerous difficulties in their daily life, and technological interventions may assist them to meet these challenges. This paper proposes an artificial intelligence-based fully automatic assistive technology to recognize different objects, and auditory inputs are provided to the user in real time, which gives better understanding to the visually impaired person about their surroundings. A deep-learning model is trained with multiple images of objects that are highly relevant to the visually impaired person. Training images are augmented and manually annotated to bring more robustness to the trained model. In addition to computer vision-based techniques for object recognition, a distance-measuring sensor is integrated to make the device more comprehensive by recognizing obstacles while navigating from one place to another. The auditory information that is conveyed to the user after scene segmentation and obstacle identification is optimized to obtain more information in less time for faster processing of video frames. The average accuracy of this proposed method is 95.19% and 99.69% for object detection and recognition, respectively. The time complexity is low, allowing a user to perceive the surrounding scene in real time.
视障人士在日常生活中面临诸多困难，技术干预或许能帮助他们应对这些挑战。本文提出一种基于人工智能的全自动辅助技术，用于识别不同物体，并实时向用户提供听觉输入，这能让视障人士更好地了解周围环境。使用与视障人士高度相关的多个物体图像训练深度学习模型。对训练图像进行增强和人工标注，以使训练后的模型更具鲁棒性。除了基于计算机视觉的物体识别技术外，还集成了一个距离测量传感器，通过在从一个地方导航到另一个地方时识别障碍物，使设备更加完善。在场景分割和障碍物识别后传达给用户的听觉信息经过优化，以便在更短的时间内获取更多信息，从而更快地处理视频帧。该方法在物体检测和识别方面的平均准确率分别为95.19%和99.69%。时间复杂度较低，允许用户实时感知周围场景。
