Author:
Andriiashen Vladyslav,van Liere Robert,van Leeuwen Tristan,Batenburg K. Joost
Abstract
AbstractAlthough X-ray imaging is used routinely in industry for high-throughput product quality control, its capability to detect internal defects has strong limitations. The main challenge stems from the superposition of multiple object features within a single X-ray view. Deep Convolutional neural networks can be trained by annotated datasets of X-ray images to detect foreign objects in real-time. However, this approach depends heavily on the availability of a large amount of data, strongly hampering the viability of industrial use with high variability between batches of products. We present a computationally efficient, CT-based approach for creating artificial single-view X-ray data based on just a few physically CT-scanned objects. By algorithmically modifying the CT-volume, a large variety of training examples is obtained. Our results show that applying the generative model to a single CT-scanned object results in image analysis accuracy that would otherwise be achieved with scans of tens of real-world samples. Our methodology leads to a strong reduction in training data needed, improved coverage of the combinations of base and foreign objects, and extensive generalizability to additional features. Once trained on just a single CT-scanned object, the resulting deep neural network can detect foreign objects in real-time with high accuracy.
Funder
Nederlandse Organisatie voor Wetenschappelijk Onderzoek
Publisher
Springer Science and Business Media LLC
Reference27 articles.
1. Haff, R. P. & Toyofuku, N. X-ray detection of defects and contaminants in the food industry. Sens. Instrum. Food Qual. Saf. 2, 262–273 (2008).
2. Mathanker, S. K., Weckler, P. R. & Bowser, T. J. X-ray applications in food and agriculture: A review. Trans. ASABE 56, 1227–1239 (2013).
3. Kotwaliwale, N. et al. X-ray imaging methods for internal quality evaluation of agricultural produce. J. Food Sci. Technol. 51, 1–15 (2014).
4. Mery, D. Computer Vision for X-ray Testing Vol. 10, 978–983 (Springer, 2015).
5. Divyanth, L., Chelladurai, V., Loganathan, M., Jayas, D. S. & Soni, P. Identification of green gram (Vigna radiata) grains infested by Callosobruchus maculatus through X-ray imaging and gan-based image augmentation. J. Biosyst. Eng. 20, 1–16 (2022).
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