Indoor home scene recognition through instance segmentation using a combination of neural networks
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: https://orcid.org/0000-0002-0180-8090, Kaewrak, Keerati, Petropoulakis, Lykourgos ORCID: https://orcid.org/0000-0003-3230-9670, Di Caterina, Gaetano ORCID: https://orcid.org/0000-0002-7256-0897 and Soraghan, John J. ORCID: https://orcid.org/0000-0003-4418-7391;-
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Item type: Conference or Workshop Item(Paper) ID code: 80793 Dates: DateEvent19 June 2022Published15 May 2022AcceptedNotes: © 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: 11 Oct 2024 00:40 URI: https://strathprints.strath.ac.uk/id/eprint/80793