Paper Info
Title
Product Surface Defect Detection Based on Deep Learning
Abstract
Image classification is a branch of computer vision that uses a computer to acquire image data and interpret them by mimicking human biological systems. This is a very important topic in today's situation because every second a large number of image data are acquired and used for various purposes around the world. One application of image classification is to detect defects on the surfaces of industrial products. Quality inspection is usually the final stage in a production line, and so far it is mainly conducted by human experts. This can be time consuming and mistake-prone. In this study, we investigate the possibility of replacing, fully or partially, human experts with a machine learner when the product defects are visible. In this study, we investigate several methods based on deep learning. The first one is to use a deep learner directly to detect the existence of defects in a given product surface image. The second one segments suspected parts first and then uses the deep learner to classify the segmented parts. The third method employs an ensemble of deep learners. Results show that the third method can provide the best results, and can be practically useful if we introduce a proper rejection mechanism.
Year
DOI
Venue
2018
10.1109/DASC/PiCom/DataCom/CyberSciTec.2018.00051
2018 IEEE 16th Intl Conf on Dependable, Autonomic and Secure Computing, 16th Intl Conf on Pervasive Intelligence and Computing, 4th Intl Conf on Big Data Intelligence and Computing and Cyber Science and Technology Congress(DASC/PiCom/DataCom/CyberSciTech)
Keywords
Field
DocType
Image classification, defect detection, convolutional neural network (CNN), support vector machine (SVM)
Kernel (linear algebra),Industrial production,Pattern recognition,Convolution,Computer science,Support vector machine,Feature extraction,Production line,Artificial intelligence,Deep learning,Contextual image classification
Conference
ISBN
Citations 
PageRank 
978-1-5386-7519-9
0
0.34
References 
Authors
5
2
Name
Order
Citations
PageRank
Po Chun Lien100.34
Qiangfu Zhao221462.36