Skip to main content

Research Repository

Advanced Search

Seeing Through the Data: A Statistical Evaluation of Prohibited Item Detection Benchmark Datasets for X-ray Security Screening

Issac-Medina, B.K.S.; Yucer, S.; Bhowmik, N.; Breckon, T.P.

Seeing Through the Data: A Statistical Evaluation of Prohibited Item Detection Benchmark Datasets for X-ray Security Screening Thumbnail


Authors



Abstract

The rapid progress in automatic prohibited object detection within the context of X-ray security screening, driven forward by advances in deep learning, has resulted in the first internationally-recognized, application-focused object detection performance standard (ECAC Common Testing Methodology for Automated Prohibited Item Detection Systems). However, the ever-increasing volume of detection work in this application area is highly reliant on a limited set of large-scale benchmark detection datasets that are specific to this domain. This study provides a comprehensive quantitative analysis of the underlying distribution of the prohibited item instances in three of the most prevalent X-ray security imagery benchmark and how these correlate against the detection performance of six state-of-the-art object detectors spanning multiple contemporary object detection paradigms. We focus on object size, location and aspect ratio within the image in addition to looking at global properties such as image colour distribution. Our results show a clear correlation between false negative (missed) detections and object size with the distribution of undetected items being statistically smaller in size than those typically found in the corresponding dataset as a whole. For false positive detections, the size distribution of such false alarm instances is shown to differ from the corresponding dataset test distribution in all cases. Furthermore, we observe that onestage, anchor-free object detectors may be more vulnerable to the detection of heavily occluded or cluttered objects than other approaches whilst the detection of smaller prohibited item instances such as bullets remains more challenging than other object types.

Citation

Issac-Medina, B., Yucer, S., Bhowmik, N., & Breckon, T. (2023, June). Seeing Through the Data: A Statistical Evaluation of Prohibited Item Detection Benchmark Datasets for X-ray Security Screening. Presented at IEEE/CVF Conference on Computer Vision and Pattern Recognition 2023, Vancouver, BC

Presentation Conference Type Conference Paper (published)
Conference Name IEEE/CVF Conference on Computer Vision and Pattern Recognition 2023
Start Date Jun 17, 2023
End Date Jun 24, 2023
Acceptance Date Apr 3, 2023
Online Publication Date Aug 14, 2023
Publication Date 2023
Deposit Date Apr 18, 2023
Publicly Available Date Aug 14, 2023
Publisher Institute of Electrical and Electronics Engineers
Book Title 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
ISBN 9798350302509
DOI https://doi.org/10.1109/CVPRW59228.2023.00059
Public URL https://durham-repository.worktribe.com/output/1133893
Publisher URL https://ieeexplore.ieee.org/xpl/conhome/1000147/all-proceedings

Files

Accepted Conference Proceeding (3.8 Mb)
PDF

Copyright Statement
© 2023 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



Downloadable Citations