Context:

This article contains an analysis of the applications of different Deep Learning and Machine Learning techniques used in a wide rangen of industries to ensure quality control in finished products through the identification of surface defects.

Method:

A systematic review of the trends and applications of Deep Learning in quality processes carried out. After consulting several databases, the articles were filtered and classified by industry and specific work technique applied to later analyze their usefulness and performance.

Results:

The results show by means of success cases the adaptability and potential applicability of this artificial intelligence technique to almost any process stage of any product, due to the handling of complementary techniques that adjust to the different particularities of the data, production processes, and quality requirements.

Conclusions:

Deep Learning, complemented with techniques such as Machine Learning or Transfer Learning, generates automated, accurate, and reliable tools to control the quality of production in all industries.

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