Abstract
The artificial intelligence has been implemented in different fields in recent years, especially in the field of image classification. The traditional pattern-based techniques present severe problems for achieving efficient algorithms with high success and are usually strongly influenced by environmental factors such as illumination, dust and movement. Convolutional neural networks are particularly effective as image classifiers, but it is a field still under research, in which new methodologies are emerging. An image classifier indicates the class with the highest probability to which the object belongs. A neural network is a dynamic entity that can be retrained to be more robust to environment changes. This implies that the behaviour of the same neural network under different training sets may be different. This difference results in a worse prediction of the classes of the model, which can lead to complete model failure. In this work, a study of the performance of different classification neural networks will be carried out by controlling the different variables in order to obtain comparable results and extrapolate the performance of these networks for a specific data set. The results of these classifiers will be studied to obtain a decision on a real classification tool for industry.
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The authors are thankful to Defta Spain SLU company.
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Ortega Sanz, D., Gómez Muñoz, C.Q., García Márquez, F.P. (2022). Convolutional Neural Networks as a Quality Control in 4.0 Industry for Screws and Nuts. In: Suma, V., Baig, Z., Kolandapalayam Shanmugam, S., Lorenz, P. (eds) Inventive Systems and Control. Lecture Notes in Networks and Systems, vol 436. Springer, Singapore. https://doi.org/10.1007/978-981-19-1012-8_2
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DOI: https://doi.org/10.1007/978-981-19-1012-8_2
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