TY - GEN
T1 - Image Classification Applied to the Detection of Leather Defects for Smart Manufacturing
AU - Ochoa-Zezatti, Alberto
AU - Cruz-Mejía, Oliverio
AU - Mejia, Jose
AU - Ceron-Monroy, Hazael
N1 - Publisher Copyright:
© 2021, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.
PY - 2021
Y1 - 2021
N2 - 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.
AB - 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.
KW - Artificial vision
KW - Convolutional neural network
KW - Footwear industry
KW - Image classification
KW - Keras
KW - Smart manufacturing
KW - Tensorflow
UR - http://www.scopus.com/inward/record.url?scp=85103248920&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-69839-3_4
DO - 10.1007/978-3-030-69839-3_4
M3 - Contribución a la conferencia
AN - SCOPUS:85103248920
SN - 9783030698386
T3 - Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST
SP - 52
EP - 61
BT - Computer Science and Health Engineering in Health Services - 4th EAI International Conference, COMPSE 2020, Proceedings
A2 - Marmolejo-Saucedo, José Antonio
A2 - Vasant, Pandian
A2 - Litvinchev, Igor
A2 - Rodriguez-Aguilar, Roman
A2 - Martinez-Rios, Felix
PB - Springer Science and Business Media Deutschland GmbH
T2 - 4th EAI International Conference on Computer Science and Health Engineering in Health Services, COMPSE 2020
Y2 - 26 November 2020 through 26 November 2020
ER -