AUTOENCODER-BASED ANOMALY DETECTION IN INDUSTRIAL X-RAY IMAGES
Paper in proceeding, 2021

Within many quality-critical industries, e.g. the aerospace industry, industrial X-ray inspection is an essential as well as a resource intense part of quality control. Within such industries the X-ray image interpretation is typically still done by humans, therefore, increasing the interpretation automatization would be of great value. We claim, that safe automatic interpretation of industrial X-ray images, requires a robust confidence estimation with respect to out-of-distribution (OOD) data. In this work we have explored if such a confidence estimation can be achieved by comparing input images with a model of the accepted images. For the image model we derived an autoencoder which we trained unsupervised on a public dataset with X-ray images of metal fusion-welds. We achieved a true positive rate at 80 − 90 % at a 4 % false positive rate, as well as correctly detected an OOD data example as an anomaly.

Author

Erik Lindgren

University West

Christopher Zach

Computer vision and medical image analysis

Proceedings of 2021 48th Annual Review of Progress in Quantitative Nondestructive Evaluation, QNDE 2021

v001t07a001
9780791885529 (ISBN)

2021 48th Annual Review of Progress in Quantitative Nondestructive Evaluation, QNDE 2021
Virtual, Online, ,

Subject Categories

Radiology, Nuclear Medicine and Medical Imaging

Computer Vision and Robotics (Autonomous Systems)

Medical Image Processing

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Latest update

2/14/2022