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Reliable Scene Recognition Approach for Mobile Robots with Limited Resources Based on Deep Learning and Neuro-Fuzzy Inference

  • Datos identificativos

    Identificador: imarina:9283520
    Autores:
    Singh APandey PPuig DNandi GCAbdel-Nasser M
    Resumen:
    Indoor scene recognition is complex due to the commonality shared between different spaces. Still, when it comes to robotics applications, the uncertainty increases due to illumination change, motion blur, interruption due to external light sources, and cluttered environments. Most existing fusion approaches do not consider the uncertainty, and others have a high computational cost that may not suit robots with limited resources. To mitigate these issues, this paper proposes a reliable indoor scene recognition approach for mobile robots with limited resources based on robust deep convolutional neural networks (CNNs) feature extractors and neuro-fuzzy inference to consider the uncertainty of the data. All CNN feature extractors are pre-trained on the Imagenet dataset and used in the manner of transfer learning. The performance of our fusion method has been assessed on a customized MIT-67 dataset and for real-time processing on a Locobot robot. We also compare the proposed method with two standard fusion methods-Early Feature Fusion (EFF) and Weighted Average Late Fusion (WALF). The experimental results demonstrate that our method achieves competitive results with a precision of 94%, and it performs well on the Locobot robot with a speed of 3.1 frames per second.
  • Otros:

    Autor según el artículo: Singh A; Pandey P; Puig D; Nandi GC; Abdel-Nasser M
    Departamento: Enginyeria Informàtica i Matemàtiques
    Autor/es de la URV: Abdelnasser Mohamed Mahmoud, Mohamed / Puig Valls, Domènec Savi / Singh, Aditya
    Palabras clave: Transfer learning Neuro-fuzzy Indoor scene recognition Fusion Deep learning Cnns transfer learning neuro-fuzzy deep learning cnns
    Resumen: Indoor scene recognition is complex due to the commonality shared between different spaces. Still, when it comes to robotics applications, the uncertainty increases due to illumination change, motion blur, interruption due to external light sources, and cluttered environments. Most existing fusion approaches do not consider the uncertainty, and others have a high computational cost that may not suit robots with limited resources. To mitigate these issues, this paper proposes a reliable indoor scene recognition approach for mobile robots with limited resources based on robust deep convolutional neural networks (CNNs) feature extractors and neuro-fuzzy inference to consider the uncertainty of the data. All CNN feature extractors are pre-trained on the Imagenet dataset and used in the manner of transfer learning. The performance of our fusion method has been assessed on a customized MIT-67 dataset and for real-time processing on a Locobot robot. We also compare the proposed method with two standard fusion methods-Early Feature Fusion (EFF) and Weighted Average Late Fusion (WALF). The experimental results demonstrate that our method achieves competitive results with a precision of 94%, and it performs well on the Locobot robot with a speed of 3.1 frames per second.
    Áreas temáticas: Engineering, electrical & electronic Electrical and electronic engineering Computer science, artificial intelligence
    Acceso a la licencia de uso: https://creativecommons.org/licenses/by/3.0/es/
    Direcció de correo del autor: mohamed.abdelnasser@urv.cat domenec.puig@urv.cat
    Identificador del autor: 0000-0002-1074-2441 0000-0002-0562-4205
    Fecha de alta del registro: 2024-10-12
    Versión del articulo depositado: info:eu-repo/semantics/publishedVersion
    Enlace a la fuente original: https://www.iieta.org/journals/ts/paper/10.18280/ts.390418
    URL Documento de licencia: https://repositori.urv.cat/ca/proteccio-de-dades/
    Referencia al articulo segun fuente origial: Trait Signal. 39 (4): 1255-1265
    Referencia de l'ítem segons les normes APA: Singh A; Pandey P; Puig D; Nandi GC; Abdel-Nasser M (2022). Reliable Scene Recognition Approach for Mobile Robots with Limited Resources Based on Deep Learning and Neuro-Fuzzy Inference. Trait Signal, 39(4), 1255-1265. DOI: 10.18280/ts.390418
    DOI del artículo: 10.18280/ts.390418
    Entidad: Universitat Rovira i Virgili
    Año de publicación de la revista: 2022
    Tipo de publicación: Journal Publications
  • Palabras clave:

    Computer Science, Artificial Intelligence,Electrical and Electronic Engineering,Engineering, Electrical & Electronic
    Transfer learning
    Neuro-fuzzy
    Indoor scene recognition
    Fusion
    Deep learning
    Cnns
    transfer learning
    neuro-fuzzy
    deep learning
    cnns
    Engineering, electrical & electronic
    Electrical and electronic engineering
    Computer science, artificial intelligence
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