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.
Professor Toby Breckon email@example.com
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.
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|
|Deposit Date||Dec 20, 2019|
|Publicly Available Date||Jun 4, 2020|
|Publisher||Institute of Electrical and Electronics Engineers|
|Book Title||2019 18th IEEE International Conference On Machine Learning And Applications (ICMLA), Boca Raton, FL, USA, 2019.|
Accepted Conference Proceeding
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