A Hybrid Neural Network Model Based on Convolutional Cascade Neural Networks: An Application for Image Inspection in Production

Diego Ortega Sanz, Carlos Quiterio Gómez Muñoz (), Guillermo Benéitez and Fausto Pedro García Márquez ()
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Diego Ortega Sanz: Universidad Autonoma de Madrid
Carlos Quiterio Gómez Muñoz: Universidad Autonoma de Madrid
Guillermo Benéitez: Universidad Europea de Madrid
Fausto Pedro García Márquez: University of Castilla-La Mancha

A chapter in Sustainability, 2023, pp 99-117 from Springer

Abstract: Abstract The field of artificial intelligence and, in particular, that which deals with artificial neural networks, is experiencing a great interest in companies for the inspection and verification of images. The current trend in the sector is to implement these novel methodologies in industrial environments so that they can benefit from their advantages over traditional systems. Quality and production managers are increasingly interested in replacing the classic inspection methods with this new approach due to its flexibility and precision. Traditional methods have some weaknesses when it comes to inspecting parts, such as sensitivity to disturbances. In an industrial environment, these disturbances can be changes in lighting during the day or during the year, the appearance of external elements such as dust or dirt. The use of new convolutional neural network techniques allows training including disturbance scenarios, teaching artificial neural network to detect non-verse defects influenced by changes in light or by the appearance of dust. In this way, it is possible to drastically reduce false-positives, avoiding costly stops in production and maximizing the precision of detection and classification of each defect. This work studies the implementation of a hybrid model based on a cascade detection neural network with a classification neural network in an industrial environment.

Keywords: Convolutional neural networks; Fault detection; Classification; Hybrid model; Deep learning (search for similar items in EconPapers)
Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:spr:isochp:978-3-031-16620-4_7

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DOI: 10.1007/978-3-031-16620-4_7

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