Indoor objects detection and recognition for an ICT mobility assistance of visually impaired people
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Indoor objects detection and recognition for an ICT mobility assistance of visually impaired people Mouna Afif 1 & Riadh Ayachi 1 & Edwige Pissaloux 2 & Yahia Said 1,3 & Mohamed Atri 4 Received: 3 November 2019 / Revised: 17 July 2020 / Accepted: 18 August 2020 # Springer Science+Business Media, LLC, part of Springer Nature 2020
Abstract
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%. Keywords Indoor object detection and recognition . Deep convolutional neural networks (DCNN) . Visually impaired people (VIP) mobility . Indoor navigation
1 Introduction Indoor object detection and indoor scene understanding are basic tasks for many applications including autonomous robot navigation [48] and mobility assistive devices for people with visual impairments (VIP) [17].
* Mouna Afif [email protected]
1
Laboratory of Electronics and Microelectronics (EμE), Faculty of Sciences of Monastir, University of Monastir, Monastir, Tunisia
2
LITIS Laboratory & CNRS FR 3638, University of Rouen Normandy Rouen, Rouen, France
3
Electrical Engineering Department, College of Engineering, Northern Border University, Arar, Saudi Arabia
4
College of Computer Science, King Khalid University, Abha, Saudi Arabia
Multimedia Tools and Applications
For independent mobility, the VIPs need to perceive relevant objects of their nearest space. As the VIPs are not able to see landmarks or such (indoor) objects, an assistive device must indicate their presence. Indoor objects’ perception in real indoor scene is a challenging task as many complex problems such as background complexity, occlusions, viewpoint changes, etc. should be taken into account. To address this problem, a fully labeled indoor object dataset was elaborated with a goal of their detection. This dataset consists of 8000 indoor images containing 16 different and the most frequent indoor landmark objects and classes. Moreover, the robotic and human navigation assistance requires a real-time processing. A Deep Convolutional Neural Networks (DCNN) may be a solution to achieve such temporal performance. Deep CNN combines two concepts: Deep Learning and Convolutional Neural Networks. Such combination integrates millions of values of parameter
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