Enhancing prohibit package detection in x-ray imagery with multi-dimensional large language model classifiers

Shaohong Chen; Yanze Ma; Baohong Gao; Langchao Qiao; Wentao Feng; Hanning Zhu; Jiaji Wu

doi:10.1117/12.3034166

13 June 2024 Enhancing prohibit package detection in x-ray imagery with multi-dimensional large language model classifiers

Shaohong Chen, Yanze Ma, Baohong Gao, Langchao Qiao, Wentao Feng, Hanning Zhu, Jiaji Wu

Author Affiliations +

Proceedings Volume 13180, International Conference on Image, Signal Processing, and Pattern Recognition (ISPP 2024); 1318018 (2024) https://doi.org/10.1117/12.3034166
Event: International Conference on Image, Signal Processing, and Pattern Recognition (ISPP 2024), 2024, Guangzhou, China

Abstract

The illicit trade of prohibit products poses significant health and economic challenges globally, prompting the need for more effective detection methods in express shipment inspections. This study introduces a novel multi-dimensional approach to prohibit package detection by combining X-ray scanning imagery with simulated express shipment information, leveraging the capabilities of both computer vision and large language models (LLMs). We employ a state-of-the-art feature extraction model, specifically a You Only Look Once (YOLO) variant, to analyse self-constructed X-ray image datasets for contraband prohibit indicators. Concurrently, we generate a simulated express information dataset, encapsulating patterns characteristic of prohibit smuggling tactics. This data is then integrated with image-derived features through custom-designed prompts to train LLM classifiers. Our methodology is unique in its consideration of multi-modal data fusion, hypothesizing that the synthesis of visual and textual information will markedly improve detection accuracy over traditional single-dimension analysis. The experimental results demonstrate the efficacy of this approach, with the LLM classifiers outperforming standard methods in accurately identifying prohibit packages. The study not only provides a new perspective on the application of LLMs in contraband detection but also sets a precedent for future research in multimodal data exploitation for security and customs enforcement.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Shaohong Chen, Yanze Ma, Baohong Gao, Langchao Qiao, Wentao Feng, Hanning Zhu, and Jiaji Wu "Enhancing prohibit package detection in x-ray imagery with multi-dimensional large language model classifiers", Proc. SPIE 13180, International Conference on Image, Signal Processing, and Pattern Recognition (ISPP 2024), 1318018 (13 June 2024); https://doi.org/10.1117/12.3034166

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KEYWORDS

X-rays

X-ray imaging

Object detection

Data modeling

Feature extraction

Information security

Education and training

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