Abstract
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.
Highlights
Vision impairment is one of the major health problems in the world
The main contribution of the proposed work is to design an artificial intelligent fully automated assistive technique for visually impaired people to perceive the objects in the surrounding and provide obstacle-aware navigation, where auditory inputs are given to users in real-time
The proposed methodology can make a significant contribution to assist visually impaired people compared to previously developed methods, which were only focused on obstacle detection and location tracking with the help of basic sensors without use of deep learning
Summary
Vision impairment is one of the major health problems in the world. Vision impairment or vision loss reduces seeing or perceiving ability, which cannot be cured through wearing glasses. The main contribution of the proposed work is to design an artificial intelligent fully automated assistive technique for visually impaired people to perceive the objects in the surrounding and provide obstacle-aware navigation, where auditory inputs are given to users in real-time. Augmentation and manual annotation are performed on the dataset to make the system robust and free from overfitting Both sensors and computer-vision based techniques are integrated to provide convenient a travel-aid to visually impaired people, through which a person can perceive multiple objects, detect obstacles and avoid collisions. The proposed methodology can make a significant contribution to assist visually impaired people compared to previously developed methods, which were only focused on obstacle detection and location tracking with the help of basic sensors without use of deep learning.
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