Indoor home scene recognition through instance segmentation using a combination of neural networks

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Basu, Amlan and Kaewrak, Keerati and Petropoulakis, Lykourgos and Di Caterina, Gaetano and Soraghan, John J. (2022) Indoor home scene recognition through instance segmentation using a combination of neural networks. In: 2022 IEEE World Conference on Applied Intelligence and Computing, 2022-06-17 - 2022-06-19, Rajkiya Engineering College.

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Abstract

This work presents a technique for recognizing indoor home scenes by using object detection. The object detection task is achieved through pre-trained Mask-RCNN (Regional Convolutional Neural Network), whilst the scene recognition is performed through a Convolutional Neural Network (CNN). The output of the Mask-RCNN is fed in input to the CNN, as this provides the CNN with the information of objects detected in one scene. So, the CNN recognizes the scene by looking at the combination of objects detected. The CNN is trained using the various object detection outputs of Mask-RCNN. This helps the CNN learn about the various combinations of objects that a scene can have. The CNN is trained using 500 combinations of 5 different scenes (bathroom, bedroom, kitchen, living room, and dining room) of the indoor home generated by Mask-RCNN. The trained network was tested on 24,000 indoor home scene images. The final accuracy produced by the CNN is 97.14%.

ORCID iDs

Basu, Amlan ORCID logoORCID: https://orcid.org/0000-0002-0180-8090, Kaewrak, Keerati, Petropoulakis, Lykourgos ORCID logoORCID: https://orcid.org/0000-0003-3230-9670, Di Caterina, Gaetano ORCID logoORCID: https://orcid.org/0000-0002-7256-0897 and Soraghan, John J. ORCID logoORCID: https://orcid.org/0000-0003-4418-7391;
  • Item type:Conference or Workshop Item(Paper)
    ID code:80793
    Dates:
    Date
    Event
    19 June 2022
    Published
    15 May 2022
    Accepted
    Notes:© 2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting /republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
    Subjects:Technology > Electrical engineering. Electronics Nuclear engineering
    Department:Faculty of Engineering > Electronic and Electrical Engineering
    Depositing user: Pure Administrator
    Date deposited:18 May 2022 13:10
    Last modified:20 Dec 2024 02:36
    URI:https://strathprints.strath.ac.uk/id/eprint/80793
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