Image Classification Applied to the Detection of Leather Defects for Smart Manufacturing

Alberto Ochoa-Zezatti, Oliverio Cruz-Mejía, Jose Mejia, Hazael Ceron-Monroy

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

Abstract

In the shoe production workshops, animal leather is used as the main raw material. Generally, an operator manually checks the surface of the leather, making sure that it does not present defects that compromise the quality of the final product. This type of inspection is subject to human error and uncontrollable factors, which represents an opportunity for the automation of the process through a system of artificial vision. A data set was developed consisting of images of animal leather, in good coordination and with defects. The digitized samples were subjected to image processing using OpenCV and Scikit-Learn, and then used in a convolutional neural network interfacing, using TensorFlow’s Keras library in Python. Finally, the trained model is capable of classifying new images into two possible groups: “Defective Leather” and “Defect-free Leather”. The trained model offers 80% predictive accuracy and 85% reliability. Although the result can be considered satisfactory, it is expected to raise the mentioned percentage with a more robust data set than the one used for the project.

Original languageEnglish
Title of host publicationComputer Science and Health Engineering in Health Services - 4th EAI International Conference, COMPSE 2020, Proceedings
EditorsJosé Antonio Marmolejo-Saucedo, Pandian Vasant, Igor Litvinchev, Roman Rodriguez-Aguilar, Felix Martinez-Rios
PublisherSpringer Science and Business Media Deutschland GmbH
Pages52-61
Number of pages10
ISBN (Print)9783030698386
DOIs
StatePublished - 1 Jan 2021
Event4th EAI International Conference on Computer Science and Health Engineering in Health Services, COMPSE 2020 - Virtual, Online
Duration: 26 Nov 202026 Nov 2020

Publication series

NameLecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST
Volume359
ISSN (Print)1867-8211
ISSN (Electronic)1867-822X

Conference

Conference4th EAI International Conference on Computer Science and Health Engineering in Health Services, COMPSE 2020
CityVirtual, Online
Period26/11/2026/11/20

Keywords

  • Artificial vision
  • Convolutional neural network
  • Footwear industry
  • Image classification
  • Keras
  • Smart manufacturing
  • Tensorflow

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