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On the Impact of Object and Sub-Component Level Segmentation Strategies for Supervised Anomaly Detection within X-Ray Security Imagery

Bhowmik, N.; Gaus, Y.F.A.; Akcay, S.; Barker, J.W.; Breckon, T.P.

On the Impact of Object and Sub-Component Level Segmentation Strategies for Supervised Anomaly Detection within X-Ray Security Imagery Thumbnail


Authors

N. Bhowmik

Y.F.A. Gaus

S. Akcay

J.W. Barker



Abstract

X-ray security screening is in widespread use to maintain transportation security against a wide range of potential threat profiles. Of particular interest is the recent focus on the use of automated screening approaches, including the potential anomaly detection as a methodology for concealment detection within complex electronic items. Here we address this problem considering varying segmentation strategies to enable the use of both object level and sub-component level anomaly detection via the use of secondary convolutional neural network (CNN) architectures. Relative performance is evaluated over an extensive dataset of exemplar cluttered X-ray imagery, with a focus on consumer electronics items. We find that sub-component level segmentation produces marginally superior performance in the secondary anomaly detection via classification stage, with true positive of ~98% of anomalies, with a ~3% false positive.

Citation

Bhowmik, N., Gaus, Y., Akcay, S., Barker, J., & Breckon, T. (2019). On the Impact of Object and Sub-Component Level Segmentation Strategies for Supervised Anomaly Detection within X-Ray Security Imagery. In 2019 18th IEEE International Conference On Machine Learning And Applications (ICMLA), Boca Raton, FL, USA, 2019 (986-991). https://doi.org/10.1109/icmla.2019.00168

Conference Name 18th IEEE International Conference on Machine Learning and Applications (ICMLA 2019)
Conference Location Boca Raton, Florida, USA
Start Date Dec 16, 2019
End Date Dec 19, 2019
Acceptance Date Sep 21, 2019
Online Publication Date Feb 17, 2020
Publication Date 2019
Deposit Date Dec 20, 2019
Publicly Available Date Mar 28, 2024
Publisher Institute of Electrical and Electronics Engineers
Pages 986-991
Book Title 2019 18th IEEE International Conference On Machine Learning And Applications (ICMLA), Boca Raton, FL, USA, 2019.
DOI https://doi.org/10.1109/icmla.2019.00168

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