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.
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References
Pacheco Vega, R.: Historia de dos ciudades: un análisis comparativo de los distritos industriales del cuero y calzado en León y Guadalajara. In: Meeting of the Latin American Studies Association (2004)
INEGI: Estadísticas a propósito de la Industria del calzado, México (2014)
Footwear - worldwide| Statista Market Forecast. https://www.statista.com/out-look/11000000/100/footwear/worldwide
Perng, D., Liu, H., Chang, C.: Automated SMD LED inspection using machine vision. Int. J. Adv. Manuf. Technol. 57, 1065–1077 (2011)
Aguilar-Torres, M., Argüelles-Cruz, A., Yánez-Márquez, C.: A real time artificial vision implementation for quality inspection of industrial products. In: 2008 Electronics, Robotics and Automotive Mechanics Conference, pp. 277–282 (2008)
Wu, M., Phoha, V.V., Moon, Y.B., Belman, A.K.: Detecting malicious defects in 3D Printing Process Using Machine Learning And Image Classification. In: ASME 2016 International Mechanical Engineering Congress and Exposition (2016)
Armesto, L., Tornero, J., Herraez, A., Asensio, J.: Inspection system based on artificial vision for paint defects detection on cars bodies. In: 2011 IEEE International Conference on Robotics and Automation, pp. 1–4. IEEE (2011)
Gnanavel, S., Manohar, S., Sridhar, K., Sokkanarayanan, S., Sathiyanarayanan, M.: Quality detection of fresh fruits and vegetables to improve horticulture and agro-industries. In: 2019 International Conference on contemporary Computing and Informatics (IC3I), pp. 268–272 (2019)
Bong, H., Truong, Q., Nguyen, H., Nguyen, M.: Vision-based inspection system for leather surface defect detection and classification. In: 2018 5th NAFOSTED Conference on Information and Computer Science (NICS), pp. 300–304 (2018)
Liong, S.T., et al.: Efficient neural network approaches for leather defect classification. arXiv preprint. (2019)
Simard, P., Steinkraus, D., Platt, J.: Best practices for convolutional neural networks applied to visual document analysis. In: Seventh International Conference on Document Analysis and Recognition, vol. 3 (2003)
Nandy, A., Biswas, M.: Reinforcement learning with keras, tensorflow, and chainerrl. In: Reinforcement Learning, pp. 129–153 (2017)
Liu, B., Wu, S., Zou, S.: Automatic detection technology of surface defects on plastic products based on machine vision. In: 2010 International Conference on Mechanic Automation and Control Engineering, pp. 2213–2216 (2010)
Perreault, S., Hébert, P.: Median filtering in constant time. IEEE Trans. Image Process. 16(9), 2389–2394 (2007)
Gupta, S., Porwal, R.: Combining laplacian and sobel gradient for greater sharpening. ICTACT J. Image Video Process. 06, 1239–1243 (2016)
Jin-Yu, Z., Yan, C., Xian-Xiang, H.: Edge detection of images based on improved Sobel operator and genetic algorithms. In: 2009 International Conference on Image Analysis and Signal Processing, pp. 31–35 (2009)
Simard, P., Steinkraus, D., Platt, J.: Best practices for convolutional neural networks applied to visual document analysis. In: Seventh International Conference on Document Analysis and Recognition, 2003, vol. 3 (2003)
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Ochoa-Zezatti, A., Cruz-Mejía, O., Mejia, J., Ceron-Monroy, H. (2021). Image Classification Applied to the Detection of Leather Defects for Smart Manufacturing. In: Marmolejo-Saucedo, J.A., Vasant, P., Litvinchev, I., Rodriguez-Aguilar, R., Martinez-Rios, F. (eds) Computer Science and Health Engineering in Health Services. COMPSE 2020. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 359. Springer, Cham. https://doi.org/10.1007/978-3-030-69839-3_4
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