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Automatic Localization of Casting Defects with Convolutional Neural Networks

Published

Author(s)

Ronay Ak, Max Ferguson, Yung-Tsun T. Lee, Kincho H. Law

Abstract

Automatic localization of defects in metal castings is a challenging task, owing to the rare occurrence and variation in appearance of defects. Convolutional neural networks (CNN) have recently shown outstanding performance in both image classification and localization tasks. We demonstrate how several different CNN architectures can be used to localize casting defects in X-ray images. We utilize data augmentation to artificially increase the size of the training dataset, allowing state-of-the-art CNN localization models to be trained on a relatively small dataset. In an alternative approach, we train a defect classification model on a series of defect images and then use a sliding classifier method to develop a simple localization model. We compare the localization accuracy and computational performance of each technique. We show promising results for defect localization on the GDXray dataset and establish a benchmark for future studies on this dataset.
Proceedings Title
2017 IEEE International Conference on Big Data (BigData 2017), 2nd Symposium on Data Analytics
for
Conference Dates
December 11-14, 2017
Conference Location
Boston, MA
Conference Title
2017 IEEE International Conference on Big Data (BigData 2017)

Keywords

Casting Defect Detection, Defect Localization, Convolutional Neural Networks, Computer Vision

Citation

Ak, R. , Ferguson, M. , Lee, Y. and Law, K. (2017), Automatic Localization of Casting Defects with Convolutional Neural Networks, 2017 IEEE International Conference on Big Data (BigData 2017), 2nd Symposium on Data Analytics for, Boston, MA, [online], https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=924455 (Accessed April 21, 2024)
Created December 11, 2017, Updated January 18, 2018