Indoor objects detection and recognition for an ICT mobility assistance of visually impaired people

Indoor object detection in real scene presents a challenging computer vision task; it is also a key component of an ICT autonomous displacement assistance of Visually Impaired People (VIP). To handle this challenge, a DCNN (Deep Convolutional Neural Networks) for indoor object detection and a new in...

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Bibliographic Details
Published inMultimedia tools and applications Vol. 79; no. 41-42; pp. 31645 - 31662
Main Authors Afif, Mouna, Ayachi, Riadh, Pissaloux, Edwige, Said, Yahia, Atri, Mohamed
Format Journal Article
LanguageEnglish
Published New York Springer US 01.11.2020
Subjects
Computer Communication Networks
Computer Science
Data Structures and Information Theory
Multimedia Information Systems
Special Purpose and Application-Based Systems
Deep convolutional neural networks (DCNN)
Visually impaired people (VIP) mobility
Indoor navigation
Indoor object detection and recognition
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Summary:Indoor object detection in real scene presents a challenging computer vision task; it is also a key component of an ICT autonomous displacement assistance of Visually Impaired People (VIP). To handle this challenge, a DCNN (Deep Convolutional Neural Networks) for indoor object detection and a new indoor dataset are proposed. The novel DCNN design is based on a pre-trained DCNN called YOLO v3. In order to train and test the proposed DCNN, a new dataset for indoor objects was created. The images of the new dataset present large variety of objects, of indoor illuminations and of indoor architectural structures potentially unsafe for a VIP independent mobility. The dataset contains about 8000 images and presents 16 indoor object categories. Experimental results prove the high performance of the proposed indoor object detection as its recognition rate (a mean average precision) is 73,19%.
ISSN:1380-7501
1573-7721
DOI:10.1007/s11042-020-09662-3