Paper Info
Title
An Evaluation of RetinaNet on Indoor Object Detection for Blind and Visually Impaired Persons Assistance Navigation
Abstract
Indoor object detection presents a computer vision task that deals with the detection of specific indoor classes. This task attracts a lot of attention, especially in the last few years. The strong interest related to this field can be explained by the big importance of this task for indoor assistance navigation for visually impaired people and also by the phenomenal development of the deep convolutional neural networks (Deep CNN). In this paper, an effort is made to perform a new indoor object detector using the deep convolutional neural network-based framework. The framework is built based on the deep convolutional neural network “RetinaNet”. Evaluation is done by using various backbones as ResNet, DenseNet, and VGGNet in order to improve detection performances and processing time. We obtained very encouraging results coming up to 84.61% mAP as detection precision.
Year
DOI
Venue
2020
10.1007/s11063-020-10197-9
Neural Processing Letters
Keywords
DocType
Volume
Indoor object recognition, Visually impaired people (VIP), Deep convolutional neural network (DCNN), Deep learning, Indoor object detection and recognition dataset (IODR)
Journal
51
Issue
ISSN
Citations 
3
1370-4621
2
PageRank 
References 
Authors
0.38
0
5
Name
Order
Citations
PageRank
Mouna Afif193.28
Riadh Ayachi2153.46
Yahia Said3217.09
Edwige Pissaloux421.05
Mohamed Atri515427.75