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Indoor/Outdoor Semantic Segmentation Using Deep Learning for Visually Impaired Wheelchair Users

Mohamed, Elhassan, Konstantin, Sirlantzis, Howells, Gareth (2021) Indoor/Outdoor Semantic Segmentation Using Deep Learning for Visually Impaired Wheelchair Users. IEEE Access, . E-ISSN 2169-3536. (doi:10.1109/ACCESS.2021.3123952) (KAR id:91157)

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Electrical Powered Wheelchair (EPW) users may find navigation through indoor and outdoor environments a significant challenge due to their disabilities. Moreover, they may suffer from near-sightedness or cognitive problems that limit their driving experience. Developing a system that can help EPW users to navigate safely by providing visual feedback and further assistance when needed can have a significant impact on the user's wellbeing. This paper presents computer vision systems based on deep learning, with an architecture based on residual blocks that can semantically segment high-resolution images. The systems are modified versions of DeepLab version 3 plus that can process high-resolution input images. Besides, they can simultaneously process images from indoor and outdoor environments, which is challenging due to the difference in data distribution and context. The proposed systems replace the base network with a smaller one and modify the encoder-decoder architecture. Nevertheless, they produce high-quality outputs with fast inference speed compared to the systems with deeper base networks. Two datasets are used to train the semantic segmentation systems: an indoor application-based dataset that has been collected and annotated manually and an outdoor dataset to cover both environments. The user can toggle between the two individual systems depending on the situation. Moreover, we proposed shared systems that automatically use a specific semantic segmentation system depending on the pixels' confidence scores. The annotated output scene is presented to the EPW user, which can aid with the user's independent navigation. State-of-the-art semantic segmentation techniques are discussed and compared. Results show the ability of the proposed systems to detect objects with sharp edges and high accuracy for indoor and outdoor environments. The developed systems are deployed on a GPU based board and then integrated on an EPW for practical usage and evaluation.

Item Type: Article
DOI/Identification number: 10.1109/ACCESS.2021.3123952
Uncontrolled keywords: CNN architecture, disabled people, deep learning, object localization, object detection, pixels classification, semantic segmentation, visually impaired users
Divisions: Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Engineering and Digital Arts
Depositing User: Elhassan Mohamed
Date Deposited: 28 Oct 2021 21:41 UTC
Last Modified: 02 Nov 2021 16:20 UTC
Resource URI: (The current URI for this page, for reference purposes)
Mohamed, Elhassan:
Konstantin, Sirlantzis:
Howells, Gareth:
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