Object Detection to Assist Visually Impaired People: A Deep Neural Network Adventure

• Published in: Advances in Visual Computing Vol. 11241; pp. 500 - 510
• Main Authors: Bashiri, Fereshteh S., LaRose, Eric, Badger, Jonathan C., D’Souza, Roshan M., Yu, Zeyun, Peissig, Peggy
• Format: Book Chapter
• Language: English
• Published: Switzerland Springer International Publishing AG 2018; Springer International Publishing
• Series: Lecture Notes in Computer Science
• Subjects: Assistive technology for visually impaired people; Data mining and knowledge discovery; Machine learning and predictive modeling; Mobile health and wearable devices
• ISBN: 3030038009; 9783030038007
• ISSN: 0302-9743; 1611-3349
• DOI: 10.1007/978-3-030-03801-4_44

Blindness or vision impairment, one of the top ten disabilities among men and women, targets more than 7 million Americans of all ages. Accessible visual information is of paramount importance to improve independence and safety of blind and visually impaired people, and there is a pressing need to develop smart automated systems to assist their navigation, specifically in unfamiliar healthcare environments, such as clinics, hospitals, and urgent cares. This contribution focused on developing computer vision algorithms composed with a deep neural network to assist visually impaired individual’s mobility in clinical environments by accurately detecting doors, stairs, and signages, the most remarkable landmarks. Quantitative experiments demonstrate that with enough number of training samples, the network recognizes the objects of interest with an accuracy of over 98% within a fraction of a second.