arXiv 2108.096032021-08cs.CV

Tensor Pooling Driven Instance Segmentation Framework for Baggage Threat Recognition

Khalifa University（哈利法大学）；Durham University（杜伦大学）；The University of Western Australia（西澳大学）；Mohamed bin Zayed University of Artificial Intelligence（穆罕默德·本·扎耶德人工智能大学）

Taimur Hassan, Samet Akcay, Mohammed Bennamoun, Salman Khan, Naoufel Werghi

备注：: Accepted in Neural Computing and Applications. Source code is available at https://github.com/taimurhassan/tensorpooling

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Automated systems designed for screening contraband items from the X-ray imagery are still facing difficulties with high clutter, concealment, and extreme occlusion. In this paper, we addressed this challenge using a novel multi-scale contour instance segmentation framework that effectively identifies the cluttered contraband data within the baggage X-ray scans. Unlike standard models that employ region-based or keypoint-based techniques to generate multiple boxes around objects, we propose to derive proposals according to the hierarchy of the regions defined by the contours. The proposed framework is rigorously validated on three public datasets, dubbed GDXray, SIXray, and OPIXray, where it outperforms the state-of-the-art methods by achieving the mean average precision score of 0.9779, 0.9614, and 0.8396, respectively. Furthermore, to the best of our knowledge, this is the first contour instance segmentation framework that leverages multi-scale information to recognize cluttered and concealed contraband data from the colored and grayscale security X-ray imagery.

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