TY - GEN
T1 - On Enhancing Anomaly Detection in X-Ray Baggage Inspection Using Persistence Homology
AU - Ahmed, Abdelfatah
AU - Velayudhan, Divya
AU - Hassan, Taimur
AU - Damiani, Ernesto
AU - Werghi, Naoufel
AU - Elfadel, Ibrahim Abe M.
PY - 2026/5/11
Y1 - 2026/5/11
N2 - Baggage screening in airports is a cornerstone of airport security measures. The advent of computer vision technologies in recent years has led to the development of several automated systems for identifying security threats in baggage scans. However, existing methods struggle with cluttered and occluded baggage items, and the scarcity of high-quality labeled data hampers their performance. Hence, in this paper, we propose a novel semi-supervised anomaly detection framework that leverages topological features for self-supervised learning. By utilizing persistence diagrams and persistence images, our approach captures the structural properties of the data, enhancing threat detection capabilities. The proposed framework employs a clustering-based self-supervised pretext task to generate pseudo labels, effectively utilizing both labeled and unlabeled data. Our framework was rigorously tested on the SIXray public dataset, achieving 86.23% accuracy and 86.05% F1-score, outperforming state-of-the-art semi-supervised methods.
AB - Baggage screening in airports is a cornerstone of airport security measures. The advent of computer vision technologies in recent years has led to the development of several automated systems for identifying security threats in baggage scans. However, existing methods struggle with cluttered and occluded baggage items, and the scarcity of high-quality labeled data hampers their performance. Hence, in this paper, we propose a novel semi-supervised anomaly detection framework that leverages topological features for self-supervised learning. By utilizing persistence diagrams and persistence images, our approach captures the structural properties of the data, enhancing threat detection capabilities. The proposed framework employs a clustering-based self-supervised pretext task to generate pseudo labels, effectively utilizing both labeled and unlabeled data. Our framework was rigorously tested on the SIXray public dataset, achieving 86.23% accuracy and 86.05% F1-score, outperforming state-of-the-art semi-supervised methods.
KW - Baggage Threat Classification
KW - Pre-text Task
KW - Semi-supervised learning
KW - Topological Features
KW - Xray Anomaly Detection
UR - https://link.springer.com/chapter/10.1007/978-3-032-19978-2_11
UR - https://www.scopus.com/pages/publications/105040590580
U2 - 10.1007/978-3-032-19978-2_11
DO - 10.1007/978-3-032-19978-2_11
M3 - Conference publication
AN - SCOPUS:105040590580
SN - 9783032199775
T3 - Lecture Notes in Computer Science (LNCS)
SP - 144
EP - 154
BT - Internet of Things, Smart Spaces, and Next Generation Networks and Systems
A2 - Koucheryavy, Yevgeni
A2 - Kumar, Neeraj
A2 - Muthanna, Ammar
A2 - Sharma, Sachin
PB - Springer, Cham
T2 - 25th International Conference on Next Generation Wired/Wireless Networks and Systems, NEW2AN 2025 and the 18th Conference on Internet of Things and Smart Spaces, ruSMART 2025
Y2 - 10 November 2025 through 12 November 2025
ER -