Abstract
The authors have been developing a design and training application with a user-friendly operation interface for CNN (Convolutional Neural Network), CAE (Convolutional Autoencoder) and SVM (Support Vector Machine), which can be used for the defect detection of various types of industrial products even without deep skills and knowledges concerning information technology. The application is required to have a visualization ability of small defects which would be the causes of classification results; however, it seems to be not easy to provide such a promising function as clearly identifying the position of defect. In this paper, CAE is applied to the visualization and position detection of such small defects included in images of industrial products. The effectiveness and promise are evaluated through visualization experiments of defective areas included in test images. Moreover, the applicability of the CAE to defect detection problems is discussed.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
Explore related subjects
Discover the latest articles, news and stories from top researchers in related subjects.References
Xie W, Wei S, Zheng Z, Yang D (2021) A CNN-based lightweight ensemble model for detecting defective carrots. J Biosyst Eng 208:287–299
Perez H, Tah JHM, Mosavi A (2019) Deep learning for detecting building defects using convolutional neural networks. J Sens 19(16):3556–3579
Nagata F, Habib MK, Watanabe K (2021) Design and implementation of convolutional neural network based SVM technique for manufacturing defect detection. Int J Mechatron Autom 8(2):53–61
Nagata F, Tokuno K, Watanabe K (2018), Design and training application for deep convolutional neural networks. In: Proceedings of joint 10th international conference on soft computing and intelligent systems and 19th international symposium on advanced intelligent systems in conjunction with intelligent systems workshop, p 618–620
Nakashima K, Nagata F (2020), Detection of defective wrap roll products using convolution neural network. In: Proceedings of 36th fuzzy system symposium, p 111–115
Selvaraju R.R, Cogswell M, Das A, Vedantam R, Parikh D, Batra D (2017) Grad-CAM: visual explanations from deep networks via gradient-based localization. In: Proceedings of 2017 IEEE international conference on computer vision, p 618–626
Simonyan K, Zisserman A (2015) Very deep convolutional networks for large-scale image recognition. In: Proceedings of international conference on learning representations
Zhuang F, Qi Z, Duan K, Xi D, Zhu Y, Zhu H, Xiong H, He Q (2021) A comprehensive survey on transfer learning. Proc IEEE 109(1):43–76
https://www.kaggle.com/datasets/ravirajsinh45/real-life-industrial-dataset-of-casting-product
Badrinarayanan V, Kendall A, Cipolla R (2017) A deep convolutional encoder-decoder architecture for image segmentation. Proc IEEE Trans Pattern Anal Mach Intell 2017:2481–2495
Hinton GE, Salakhutdinov RR (2006) Reducing the dimensionality of data with neural networks. J Sci 313(5786):504–507
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
This work was presented in part at the joint symposium of the 27th International Symposium on Artificial Life and Robotics, the 7th International Symposium on BioComplexity, and the 5th International Symposium on Swarm Behavior and Bio-Inspired Robotics (Online, January 25–27, 2022).
About this article
Cite this article
Arima, K., Nagata, F., Shimizu, T. et al. Visualization and location estimation of defective parts of industrial products using convolutional autoencoder. Artif Life Robotics 27, 804–811 (2022). https://doi.org/10.1007/s10015-022-00797-0
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10015-022-00797-0