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.
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