Improving Classification Accuracy in Paint Surface Inspection Using Image Processing and Deep Learning

Motoki Sawada, Yuta Fuji, Naoyuki Aikawa

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

Abstract

In recent years, research has been conducted on defect detection and classification using image processing and deep learning as alternatives to visual inspection. This paper proposes a method to detect defects by applying embossing and histogram equalization to images of painted surfaces and classify defects using ResNet50. The proposed method is capable of simultaneously classifying multiple defects in an image. Furthermore, we show that the proposed method can improve the accuracy of defect extraction compared to the conventional method.

Original languageEnglish
Title of host publication2024 IEEE 67th International Midwest Symposium on Circuits and Systems, MWSCAS 2024
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages707-711
Number of pages5
ISBN (Electronic)9798350387179
DOIs
Publication statusPublished - 2024
Event67th IEEE International Midwest Symposium on Circuits and Systems, MWSCAS 2024 - Springfield, United States
Duration: 11 Aug 202414 Aug 2024

Publication series

NameMidwest Symposium on Circuits and Systems
ISSN (Print)1548-3746

Conference

Conference67th IEEE International Midwest Symposium on Circuits and Systems, MWSCAS 2024
Country/TerritoryUnited States
CitySpringfield
Period11/08/2414/08/24

Keywords

  • deep learning
  • defect classification
  • defect detection
  • embossing

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