Identificador persistente para citar o vincular este elemento: http://hdl.handle.net/10553/77405
Título: Efficient Multi-Object Detection and Smart Navigation Using Artificial Intelligence for Visually Impaired People
Autores/as: Joshi, Rakesh Chandra
Yadav, Saumya
Dutta, Malay Kishore
Travieso González, Carlos Manuel 
Clasificación UNESCO: 3307 Tecnología electrónica
Palabras clave: Artificial intelligence
Assistive systems
Computer vision
Deep learning
Machine learning, et al.
Fecha de publicación: 2020
Proyectos: Grants from Department of Science and Technology, Government of India, grant number SEED/TIDE/2018/6/G
Publicación seriada: Entropy 
Resumen: Visually impaired people face numerous di culties 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 di erent 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.
URI: http://hdl.handle.net/10553/77405
ISSN: 1099-4300
DOI: 10.3390/e22090941
Fuente: Entropy [ISSN 1099-4300], v. 22(9), 941
Colección:Artículos
miniatura
Adobe PDF (4,57 MB)
Vista completa

Google ScholarTM

Verifica

Altmetric


Comparte



Exporta metadatos



Los elementos en ULPGC accedaCRIS están protegidos por derechos de autor con todos los derechos reservados, a menos que se indique lo contrario.