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Using Deep Learning to Detect Defects in Manufacturing: A Comprehensive Survey and Current Challenges

by 1,2, 1,2,3,*, 1, 1, 1 and 3
1
School of Mechanical Engineering, Guizhou University, Guiyang 550025, China
2
Guizhou Provincial Key Laboratory of Public Big Data, Guizhou University, Guiyang 550025, China
3
Key Laboratory of Advanced Manufacturing Technology of Ministry of Education, Guizhou University, Guiyang 550025, China
*
Author to whom correspondence should be addressed.
Materials 2020, 13(24), 5755; https://doi.org/10.3390/ma13245755
Received: 3 November 2020 / Revised: 5 December 2020 / Accepted: 7 December 2020 / Published: 16 December 2020
The detection of product defects is essential in quality control in manufacturing. This study surveys stateoftheart deep-learning methods in defect detection. First, we classify the defects of products, such as electronic components, pipes, welded parts, and textile materials, into categories. Second, recent mainstream techniques and deep-learning methods for defects are reviewed with their characteristics, strengths, and shortcomings described. Third, we summarize and analyze the application of ultrasonic testing, filtering, deep learning, machine vision, and other technologies used for defect detection, by focusing on three aspects, namely method and experimental results. To further understand the difficulties in the field of defect detection, we investigate the functions and characteristics of existing equipment used for defect detection. The core ideas and codes of studies related to high precision, high positioning, rapid detection, small object, complex background, occluded object detection and object association, are summarized. Lastly, we outline the current achievements and limitations of the existing methods, along with the current research challenges, to assist the research community on defect detection in setting a further agenda for future studies. View Full-Text
Keywords: defect detection; quality control; deep learning; object detection defect detection; quality control; deep learning; object detection
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MDPI and ACS Style

Yang, J.; Li, S.; Wang, Z.; Dong, H.; Wang, J.; Tang, S. Using Deep Learning to Detect Defects in Manufacturing: A Comprehensive Survey and Current Challenges. Materials 2020, 13, 5755. https://doi.org/10.3390/ma13245755

AMA Style

Yang J, Li S, Wang Z, Dong H, Wang J, Tang S. Using Deep Learning to Detect Defects in Manufacturing: A Comprehensive Survey and Current Challenges. Materials. 2020; 13(24):5755. https://doi.org/10.3390/ma13245755

Chicago/Turabian Style

Yang, Jing, Shaobo Li, Zheng Wang, Hao Dong, Jun Wang, and Shihao Tang. 2020. "Using Deep Learning to Detect Defects in Manufacturing: A Comprehensive Survey and Current Challenges" Materials 13, no. 24: 5755. https://doi.org/10.3390/ma13245755

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