A systematic review of deep learning approaches for surface defect detection in industrial applications

Author:

Ameri Rasoul,Hsu Chung-Chian,Band Shahab S.

Funder

Ministry of Education

Publisher

Elsevier BV

Subject

Electrical and Electronic Engineering,Artificial Intelligence,Control and Systems Engineering

Reference107 articles.

1. Energy consumption optimization in wastewater treatment plants: Machine learning for monitoring incineration of sewage sludge;Adibimanesh;Sustain. Energy Technol. Assess.,2023

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3. FDD: A deep learning–based steel defect detectors;Akhyar;Int. J. Adv. Manuf. Technol.,2023

4. Prediction of maximum tensile stress in plain-weave composite laminates with interacting holes via stacked machine learning algorithms: A comparative study;Bagherzadeh;Mech. Syst. Signal Process.,2023

5. Bergmann, Paul, Fauser, Michael, Sattlegger, David, Steger, Carsten, 2019. MVTec AD–A comprehensive real-world dataset for unsupervised anomaly detection. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 9592–9600.

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