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Specular reflection Surface Defects Detection by using Deep Learning

Authors:

Zhong Zhang,

Borui Zhang,

Takuma AkidukiAuthors Info & Claims

ICISDM '19: Proceedings of the 2019 3rd International Conference on Information System and Data Mining

Pages 6 - 10

https://doi.org/10.1145/3325917.3325930

Published: 06 April 2019 Publication History

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Abstract

As you know that defects inspection of specular surface is very difficult because its specular reflection is very strong and defects' reflection is weaker. And the existing computer vision-based industrial parts surface defect detection methods are limited by environmental factors, and the image preprocessing process is complex. On the other hand, with the rapid development of Convolutional Neural Networks (CNN) that is one type of deep learning and has excellent performance for image processing, has led to the rapid development of computer vision research based on deep learning. In this paper, we proposed an ensemble CNN in which integrated two convolutional neural network models for surface defect detection, and obtained better results.

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Han HTian LLi MCui XShang CHou S(2023)Design of Rail Surface Defect Detection System Based on LabVIEW Machine Vision2023 IEEE 6th Information Technology,Networking,Electronic and Automation Control Conference (ITNEC)10.1109/ITNEC56291.2023.10082124(207-211)Online publication date: 24-Feb-2023
https://doi.org/10.1109/ITNEC56291.2023.10082124
Bin Roslan MIbrahim ZAziz Z(2022)Real-Time Plastic Surface Defect Detection Using Deep Learning2022 IEEE 12th Symposium on Computer Applications & Industrial Electronics (ISCAIE)10.1109/ISCAIE54458.2022.9794475(111-116)Online publication date: 21-May-2022
https://doi.org/10.1109/ISCAIE54458.2022.9794475
Procházka ACharvát JVyšata OMandic D(2022)Incremental deep learning for reflectivity data recognition in stomatologyNeural Computing and Applications10.1007/s00521-021-06842-634:9(7081-7089)Online publication date: 1-May-2022
https://dl.acm.org/doi/10.1007/s00521-021-06842-6

Index Terms

Specular reflection Surface Defects Detection by using Deep Learning
1. Networks
  1. Network algorithms
    1. Network economics

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ICISDM '19: Proceedings of the 2019 3rd International Conference on Information System and Data Mining

April 2019

251 pages

ISBN:9781450366359

DOI:10.1145/3325917

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 06 April 2019

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ICISDM 2019: 2019 The 3rd International Conference on Information System and Data Mining

April 6 - 8, 2019

TX, Houston, USA

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Han HTian LLi MCui XShang CHou S(2023)Design of Rail Surface Defect Detection System Based on LabVIEW Machine Vision2023 IEEE 6th Information Technology,Networking,Electronic and Automation Control Conference (ITNEC)10.1109/ITNEC56291.2023.10082124(207-211)Online publication date: 24-Feb-2023
https://doi.org/10.1109/ITNEC56291.2023.10082124
Bin Roslan MIbrahim ZAziz Z(2022)Real-Time Plastic Surface Defect Detection Using Deep Learning2022 IEEE 12th Symposium on Computer Applications & Industrial Electronics (ISCAIE)10.1109/ISCAIE54458.2022.9794475(111-116)Online publication date: 21-May-2022
https://doi.org/10.1109/ISCAIE54458.2022.9794475
Procházka ACharvát JVyšata OMandic D(2022)Incremental deep learning for reflectivity data recognition in stomatologyNeural Computing and Applications10.1007/s00521-021-06842-634:9(7081-7089)Online publication date: 1-May-2022
https://dl.acm.org/doi/10.1007/s00521-021-06842-6

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Automatic detection of surface defects based on deep random chains

Surface Reflection: Physical and Geometrical Perspectives

Surface Identification Using the Dichromatic Reflection Model

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Automatic detection of surface defects based on deep random chains

Surface Reflection: Physical and Geometrical Perspectives

Surface Identification Using the Dichromatic Reflection Model

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