Dual-view X-ray Detection: Can AI Detect Prohibited Items from Dual-view X-ray Images like Humans?

• URL: http://arxiv.org/abs/2411.18082v1
• Date: Wed, 27 Nov 2024 06:36:20 GMT
• Title: Dual-view X-ray Detection: Can AI Detect Prohibited Items from Dual-view X-ray Images like Humans?
• Authors: Renshuai Tao, Haoyu Wang, Yuzhe Guo, Hairong Chen, Li Zhang, Xianglong Liu, Yunchao Wei, Yao Zhao,
• Abstract summary: We introduce the Large-scale Dual-view X-ray (LDXray), which consists of 353,646 instances across 12 categories. To emulate human intelligence in dual-view detection, we propose the Auxiliary-view Enhanced Network (AENet) Experiments on the LDXray dataset demonstrate that the dual-view mechanism significantly enhances detection performance.
• Score: 78.26435264182763
• License:
• Abstract: To detect prohibited items in challenging categories, human inspectors typically rely on images from two distinct views (vertical and side). Can AI detect prohibited items from dual-view X-ray images in the same way humans do? Existing X-ray datasets often suffer from limitations, such as single-view imaging or insufficient sample diversity. To address these gaps, we introduce the Large-scale Dual-view X-ray (LDXray), which consists of 353,646 instances across 12 categories, providing a diverse and comprehensive resource for training and evaluating models. To emulate human intelligence in dual-view detection, we propose the Auxiliary-view Enhanced Network (AENet), a novel detection framework that leverages both the main and auxiliary views of the same object. The main-view pipeline focuses on detecting common categories, while the auxiliary-view pipeline handles more challenging categories using ``expert models" learned from the main view. Extensive experiments on the LDXray dataset demonstrate that the dual-view mechanism significantly enhances detection performance, e.g., achieving improvements of up to 24.7% for the challenging category of umbrellas. Furthermore, our results show that AENet exhibits strong generalization across seven different detection models for X-ray Inspection

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