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Artificial Intelligence Review

Erschienen in:

21.04.2023

A survey of real-time surface defect inspection methods based on deep learning

verfasst von: Yi Liu, Changsheng Zhang, Xingjun Dong

Erschienen in: Artificial Intelligence Review | Ausgabe 10/2023

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Abstract

In recent years, deep learning methods have been widely used in various industrial scenarios, promoting industrial intelligence. Real-time surface defect inspection of industrial products is one of the research focuses in industry. Surface defect inspection methods based on deep learning show great advantages and make it possible to detect defects in real time with high accuracy. From the perspective of real-time inspection, according to different types of surfaces in industry, this paper reviews the latest deep learning-based surface defect inspection methods from three levels: defect classification, defect detection and defect segmentation. After that, this paper introduces commonly used metrics for evaluating the performance of surface defect inspection models and public surface defect datasets. Then, this paper discusses the challenges faced by deep learning-based real-time surface defect inspection methods, including the acquisition of surface defect datasets, balancing the accuracy and speed of inspection models, and the application in industrial environments. Finally, this paper provides an outlook on the future development trend of surface defect inspection.

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Titel: A survey of real-time surface defect inspection methods based on deep learning
verfasst von: Yi Liu
Changsheng Zhang
Xingjun Dong
Publikationsdatum: 21.04.2023
Verlag: Springer Netherlands
Erschienen in: Artificial Intelligence Review / Ausgabe 10/2023
Print ISSN: 0269-2821
Elektronische ISSN: 1573-7462
DOI: https://doi.org/10.1007/s10462-023-10475-7

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