Dr Neelanjan Bhowmik neelanjan.bhowmik@durham.ac.uk
Post Doctoral Research Associate
Joint Sub-component Level Segmentation and Classification for Anomaly Detection within Dual-Energy X-Ray Security Imagery
Bhowmik, N.; Breckon, T.P.
Authors
Professor Toby Breckon toby.breckon@durham.ac.uk
Professor
Abstract
X-ray baggage security screening is in widespread use and crucial to maintaining transport security for threat/anomaly detection tasks. The automatic detection of anomaly, which is concealed within cluttered and complex electronics/electrical items, using 2D X-ray imagery is of primary interest in recent years. We address this task by introducing joint object sub-component level segmentation and classification strategy using deep Convolution Neural Network architecture. The performance is evaluated over a dataset of cluttered X-ray baggage security imagery, consisting of consumer electrical and electronics items using variants of dual-energy X-ray imagery (pseudo-colour, high, low, and effective-Z). The proposed joint sub-component level segmentation and classification approach achieve ∼ 99% true positive and ∼ 5% false positive for anomaly detection task.
Citation
Bhowmik, N., & Breckon, T. (2022). Joint Sub-component Level Segmentation and Classification for Anomaly Detection within Dual-Energy X-Ray Security Imagery.
Conference Name | International Conference on Machine Learning Applications |
---|---|
Conference Location | Bahamas |
Start Date | Dec 12, 2022 |
End Date | Dec 14, 2022 |
Acceptance Date | Sep 5, 2022 |
Publication Date | 2022-12 |
Deposit Date | Nov 2, 2022 |
Publicly Available Date | Dec 15, 2022 |
Publisher | Institute of Electrical and Electronics Engineers |
Publisher URL | https://ieeexplore.ieee.org/xpl/conhome/1001544/all-proceedings |
Related Public URLs | https://doi.org/10.48550/arXiv.2210.16453 |
Files
Accepted Conference Proceeding
(6.2 Mb)
PDF
Copyright Statement
© 2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
You might also like
Region-based Appearance and Flow Characteristics for Anomaly Detection in Infrared Surveillance Imagery
(2023)
Conference Proceeding
Lost in Compression: the Impact of Lossy Image Compression on Variable Size Object Detection within Infrared Imagery
(2022)
Conference Proceeding
Cross-modal Image Synthesis in Dual-Energy X-Ray Security Imagery
(2022)
Conference Proceeding
On the Impact of Using X-Ray Energy Response Imagery for Object Detection via Convolutional Neural Networks
(2021)
Conference Proceeding
Downloadable Citations
About Durham Research Online (DRO)
Administrator e-mail: dro.admin@durham.ac.uk
This application uses the following open-source libraries:
SheetJS Community Edition
Apache License Version 2.0 (http://www.apache.org/licenses/)
PDF.js
Apache License Version 2.0 (http://www.apache.org/licenses/)
Font Awesome
SIL OFL 1.1 (http://scripts.sil.org/OFL)
MIT License (http://opensource.org/licenses/mit-license.html)
CC BY 3.0 ( http://creativecommons.org/licenses/by/3.0/)
Powered by Worktribe © 2024
Advanced Search