A New Few-Shot Learning-Based Model for Prohibited Objects Detection in Cluttered Baggage X-Ray Images Through Edge Detection and Reverse Validation

Liu, K; Lyu, S; Shivakumara, P; Blumenstein, M; Lu, Y

A New Few-Shot Learning-Based Model for Prohibited Objects Detection in Cluttered Baggage X-Ray Images Through Edge Detection and Reverse Validation

Liu, K Lyu, S Shivakumara, P Blumenstein, M

Lu, Y

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Publisher:: IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Publication Type:: Journal Article
Citation:: IEEE Signal Processing Letters, 2023, 30, pp. 1607-1611
Issue Date:: 2023-01-01

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Field	Value	Language
dc.contributor.author	Liu, K
dc.contributor.author	Lyu, S
dc.contributor.author	Shivakumara, P
dc.contributor.author	Blumenstein, M https://orcid.org/0000-0002-9908-3744
dc.contributor.author	Lu, Y
dc.date.accessioned	2024-02-15T04:07:44Z
dc.date.available	2024-02-15T04:07:44Z
dc.date.issued	2023-01-01
dc.identifier.citation	IEEE Signal Processing Letters, 2023, 30, pp. 1607-1611
dc.identifier.issn	1070-9908
dc.identifier.issn	1558-2361
dc.identifier.uri	http://hdl.handle.net/10453/175686
dc.description.abstract	Detecting prohibited items via X-ray screening at airports and sensitive venues is essential for preventing smuggling and breaches of security. The difficulty in prohibited items inspection lies in accurately detecting prohibited items in complex X-ray images and limited access to X-ray images containing prohibited items. Few-shot detection aims at learning with limited examples and assigning a category label to each object. However, most few-shot learning methods do not focus on the edge information of the occluded object in X-ray images, which is crucial for the model to detect prohibited items in the X-ray images. In this paper, we presents a method (RVViT) for few-shot prohibited items detection tasks which fully acknowledges the significance of X-ray penetrability and increases the stability of few-shot learning model. Specifically, a Transformer encoder is firstly adopted for generating high-level semantic features that contain global information. At the same time, an edge detection module is devised for enhancing the edge information of prohibited items. Moreover, to further improve the stability of the few-shot learning model and ensure prototype consistency between the support and query samples, a reverse validation strategy is proposed to assist training. Extensive experiments demonstrate our method outperforms state-of-the-art approaches in terms of detection with a small number of samples.
dc.language	English
dc.publisher	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
dc.relation.ispartof	IEEE Signal Processing Letters
dc.relation.isbasedon	10.1109/LSP.2023.3326088
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	0801 Artificial Intelligence and Image Processing, 0906 Electrical and Electronic Engineering, 1005 Communications Technologies
dc.subject.classification	Networking & Telecommunications
dc.subject.classification	4006 Communications engineering
dc.subject.classification	4009 Electronics, sensors and digital hardware
dc.subject.classification	4603 Computer vision and multimedia computation
dc.title	A New Few-Shot Learning-Based Model for Prohibited Objects Detection in Cluttered Baggage X-Ray Images Through Edge Detection and Reverse Validation
dc.type	Journal Article
utslib.citation.volume	30
utslib.for	0801 Artificial Intelligence and Image Processing
utslib.for	0906 Electrical and Electronic Engineering
utslib.for	1005 Communications Technologies
pubs.organisational-group	University of Technology Sydney
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	University of Technology Sydney/Strength - AAII - Australian Artificial Intelligence Institute
pubs.organisational-group	University of Technology Sydney/Strength - QSI - Centre for Quantum Software and Information
utslib.copyright.status	closed_access	*
dc.date.updated	2024-02-15T04:07:43Z
pubs.publication-status	Published
pubs.volume	30

Abstract:

Detecting prohibited items via X-ray screening at airports and sensitive venues is essential for preventing smuggling and breaches of security. The difficulty in prohibited items inspection lies in accurately detecting prohibited items in complex X-ray images and limited access to X-ray images containing prohibited items. Few-shot detection aims at learning with limited examples and assigning a category label to each object. However, most few-shot learning methods do not focus on the edge information of the occluded object in X-ray images, which is crucial for the model to detect prohibited items in the X-ray images. In this paper, we presents a method (RVViT) for few-shot prohibited items detection tasks which fully acknowledges the significance of X-ray penetrability and increases the stability of few-shot learning model. Specifically, a Transformer encoder is firstly adopted for generating high-level semantic features that contain global information. At the same time, an edge detection module is devised for enhancing the edge information of prohibited items. Moreover, to further improve the stability of the few-shot learning model and ensure prototype consistency between the support and query samples, a reverse validation strategy is proposed to assist training. Extensive experiments demonstrate our method outperforms state-of-the-art approaches in terms of detection with a small number of samples.

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http://hdl.handle.net/10453/175686