Open AccessApplication of deep learning in defect Detection
Author(s) -
Xiaoyuan Gong,
Yuewei Bai,
Yiqun Liu,
Hua Mao
Publication year - 2020
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1684/1/012030
Subject(s) - field (mathematics) , combing , manufacturing engineering , computer science , process (computing) , product (mathematics) , deep learning , new product development , artificial intelligence , engineering , industrial engineering , business , materials science , geometry , mathematics , marketing , pure mathematics , composite material , operating system
Defect detection has been the important link in the process of manufacturing enterprise production, is also one of the challenging parts, with the rapid development of science and technology and the introduction of [[CHECK_DOUBLEQUOT_ENT]] Industry 4.0 ", Intelligent Manufacturing, “Made in China 2025” put forward of the concept and development, manufacturing enterprises for the industrial product defect detection requirements are increasingly high, industrial product defect detection has also received more and more attention. In this paper, the application of deep learning in defect detection of industrial products is analyzed and discussed. Meanwhile, the traditional defect detection methods are summarized and compared with those using deep learning method. By combing and analyzing ICCV2019, the top conference in the field of computer vision, new technologies, new methods and new ideas that may be applied in the field of defect detection in the future were explored, and the challenges faced by them were analyzed in depth.