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

• Published in: Materials Vol. 13; no. 24; p. 5755
• Main Authors: Yang, Jing, Li, Shaobo, Wang, Zheng, Dong, Hao, Wang, Jun, Tang, Shihao
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 16.12.2020; MDPI
• Subjects: Accuracy; Deep learning; Defects; Electronic components; Fault diagnosis; Machine vision; Magnetic fields; Manufacturing; Neural networks; Object recognition; Porous materials; Product quality; Quality control; Review; Ultrasonic testing; Vision systems; deep learning; object detection; defect detection; quality control
• ISSN: 1996-1944; 1996-1944
• DOI: 10.3390/ma13245755

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.