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A pixel-wise annotated dataset of small overlooked indoor objects for semantic segmentation applications

Mohamed, Elhassan, Sirlantzis, Konstantinos, Howells, Gareth (2022) A pixel-wise annotated dataset of small overlooked indoor objects for semantic segmentation applications. Data in Brief, 40 . Article Number 107791. ISSN 2352-3409. (doi:10.1016/j.dib.2022.107791) (KAR id:92598)

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Official URL
https://doi.org/10.1016/j.dib.2022.107791

Abstract

The purpose of the dataset is to provide annotated images for pixel classification tasks with application to powered wheelchair users. As some of the widely available datasets contain only general objects, we introduced this dataset to cover the missing pieces, which can be considered as application-specific objects. However, these objects of interest are not only important for powered wheelchair users but also for indoor navigation and environmental understanding in general. For example, indoor assistive and service robots need to comprehend their surroundings to ease navigation and interaction with different size objects. The proposed dataset is recorded using a camera installed on a powered wheelchair. The camera is installed beneath the joystick so that it can have a clear vision with no obstructions from the user's body or legs. The powered wheelchair is then driven through the corridors of the indoor environment, and a one-minute video is recorded. The collected video is annotated on the pixel level for semantic segmentation (pixel classification) tasks. Pixels of different objects are annotated using MATLAB software. The dataset has various object sizes (small, medium, and large), which can explain the variation of the pixel's distribution in the dataset. Usually, Deep Convolutional Neural Networks (DCNNs) that perform well on large-size objects fail to produce accurate results on small-size objects. Whereas training a DCNN on a multi-size objects dataset can build more robust systems. Although the recorded objects are vital for many applications, we have included more images of different kinds of door handles with different angles, orientations, and illuminations as they are rare in the publicly available datasets. The proposed dataset has 1549 images and covers nine different classes. We used the dataset to train and test a semantic segmentation system that can aid and guide visually impaired users by providing visual cues.

Item Type: Article
DOI/Identification number: 10.1016/j.dib.2022.107791
Uncontrolled keywords: Semantic segmentation; Indoor objects; Door handles; dataset; Deep learning; Pixels classification; Convolutional neural network
Divisions: Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Engineering and Digital Arts
Depositing User: Elhassan Mohamed
Date Deposited: 07 Jan 2022 21:38 UTC
Last Modified: 10 Jan 2022 13:25 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/92598 (The current URI for this page, for reference purposes)
Mohamed, Elhassan: https://orcid.org/0000-0001-9746-1564
Sirlantzis, Konstantinos: https://orcid.org/0000-0002-0847-8880
Howells, Gareth: https://orcid.org/0000-0001-5590-0880
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