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Open AccessArticle

Vision-Based Novelty Detection Using Deep Features and Evolved Novelty Filters for Specific Robotic Exploration and Inspection Tasks

1
Department of Electronics Engineering, University of Guanajuato, Campus Irapuato-Salamanca, Carr. Salamanca-Valle de Santiago Km 3.5 + 1.8, Comunidad de Palo Blanco, Salamanca 36885, Mexico
2
Department of Art and Enterprise, University of Guanajuato, Campus Irapuato-Salamanca, Carr. Salamanca-Valle de Santiago Km 3.5 + 1.8, Comunidad de Palo Blanco, Salamanca 36885, Mexico
*
Author to whom correspondence should be addressed.
Sensors 2019, 19(13), 2965; https://doi.org/10.3390/s19132965
Received: 8 May 2019 / Revised: 18 June 2019 / Accepted: 28 June 2019 / Published: 5 July 2019
One of the essential abilities in animals is to detect novelties within their environment. From the computational point of view, novelty detection consists of finding data that are different in some aspect to the known data. In robotics, researchers have incorporated novelty modules in robots to develop automatic exploration and inspection tasks. The visual sensor is one of the preferred sensors to perform this task. However, there exist problems as illumination changes, occlusion, and scale, among others. Besides, novelty detectors vary their performance depending on the specific application scenario. In this work, we propose a visual novelty detection framework for specific exploration and inspection tasks based on evolved novelty detectors. The system uses deep features to represent the visual information captured by the robots and applies a global optimization technique to design novelty detectors for specific robotics applications. We verified the performance of the proposed system against well-established state-of-the-art methods in a challenging scenario. This scenario was an outdoor environment covering typical problems in computer vision such as illumination changes, occlusion, and geometric transformations. The proposed framework presented high-novelty detection accuracy with competitive or even better results than the baseline methods. View Full-Text
Keywords: visual inspection; one-class classifier; grow-when-required neural network; evolving connectionist systems; automatic design; bio-inspired techniques; artificial bee colony visual inspection; one-class classifier; grow-when-required neural network; evolving connectionist systems; automatic design; bio-inspired techniques; artificial bee colony
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MDPI and ACS Style

Contreras-Cruz, M.A.; Ramirez-Paredes, J.P.; Hernandez-Belmonte, U.H.; Ayala-Ramirez, V. Vision-Based Novelty Detection Using Deep Features and Evolved Novelty Filters for Specific Robotic Exploration and Inspection Tasks. Sensors 2019, 19, 2965. https://doi.org/10.3390/s19132965

AMA Style

Contreras-Cruz MA, Ramirez-Paredes JP, Hernandez-Belmonte UH, Ayala-Ramirez V. Vision-Based Novelty Detection Using Deep Features and Evolved Novelty Filters for Specific Robotic Exploration and Inspection Tasks. Sensors. 2019; 19(13):2965. https://doi.org/10.3390/s19132965

Chicago/Turabian Style

Contreras-Cruz, Marco A.; Ramirez-Paredes, Juan P.; Hernandez-Belmonte, Uriel H.; Ayala-Ramirez, Victor. 2019. "Vision-Based Novelty Detection Using Deep Features and Evolved Novelty Filters for Specific Robotic Exploration and Inspection Tasks" Sensors 19, no. 13: 2965. https://doi.org/10.3390/s19132965

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