Autor segons l'article: Singh A; Pandey P; Puig D; Nandi GC; Abdel-Nasser M
Departament: Enginyeria Informàtica i Matemàtiques
Autor/s de la URV: Abdelnasser Mohamed Mahmoud, Mohamed / Puig Valls, Domènec Savi
Paraules clau: Transfer learning Neuro-fuzzy Indoor scene recognition Fusion Deep learning Cnns transfer learning neuro-fuzzy deep learning cnns
Resum: 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.
Àrees temàtiques: Engineering, electrical & electronic Electrical and electronic engineering Computer science, artificial intelligence
Accès a la llicència d'ús: https://creativecommons.org/licenses/by/3.0/es/
Adreça de correu electrònic de l'autor: mohamed.abdelnasser@urv.cat domenec.puig@urv.cat
Identificador de l'autor: 0000-0002-1074-2441 0000-0002-0562-4205
Data d'alta del registre: 2023-05-14
Versió de l'article dipositat: info:eu-repo/semantics/publishedVersion
Enllaç font original: https://www.iieta.org/journals/ts/paper/10.18280/ts.390418
URL Document de llicència: http://repositori.urv.cat/ca/proteccio-de-dades/
Referència a l'article segons font original: Trait Signal. 39 (4): 1255-1265
Referència 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 de l'article: 10.18280/ts.390418
Entitat: Universitat Rovira i Virgili
Any de publicació de la revista: 2022
Tipus de publicació: Journal Publications
Repositori URV