Defect detection on randomly textured surfaces by convolutional neural networks

S. Y. Jung, Y. H. Tsai, W. Y. Chiu, Jwu-Sheng Hu, Chuen-Tsai Sun

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

8 Scopus citations

Abstract

Automatically detecting the defects on the randomly textured surfaces for industrial purpose is a demanding procedure due to the ambiguity between defects and textures, lack of defect-labeled data and the must-have extreme accuracy. In this paper we proposed a procedure as the beginning of automating the defect detection on woods with randomly textured surfaces by employing 3 different architectures of convolutional neural networks. The deep convolutional neural network resulted in 99.80% accuracy, discriminating among normal wood and the other 4 types of defects images. The models were evaluated and understood by visualizing the saliency maps. The results from our work implies that other industrial images with defects on randomly textured surfaces may apply the similar procedures to accelerate the automating of defect detection and progressing of industry 4.0.

Original languageEnglish
Title of host publicationAIM 2018 - IEEE/ASME International Conference on Advanced Intelligent Mechatronics
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1456-1461
Number of pages6
ISBN (Print)9781538618547
DOIs
StatePublished - 30 Aug 2018
Event2018 IEEE/ASME International Conference on Advanced Intelligent Mechatronics, AIM 2018 - Auckland, New Zealand
Duration: 9 Jul 201812 Jul 2018

Publication series

NameIEEE/ASME International Conference on Advanced Intelligent Mechatronics, AIM
Volume2018-July

Conference

Conference2018 IEEE/ASME International Conference on Advanced Intelligent Mechatronics, AIM 2018
CountryNew Zealand
CityAuckland
Period9/07/1812/07/18

Fingerprint Dive into the research topics of 'Defect detection on randomly textured surfaces by convolutional neural networks'. Together they form a unique fingerprint.

Cite this