Abstract
Prohibited item detection in X-ray security inspection images using computer vision technology is a challenging task in real world scenarios due to various factors, include occlusion and unfriendly imaging viewing angle. Intelligent analysis of multi-view X-ray security inspection images is a relatively direct and targeted solution. However, there is currently no published multi-view X-ray security inspection image dataset. In this paper, we construct a dual-view X-ray security inspection dataset, named Dualray, based on real acquisition method. Dualray dataset consists of 4371 pairs of images with 6 categories of prohibited items, and each pair of instances is imaged from horizontal and vertical viewing angles. We have annotated each sample with the categories of prohibited item and the location represented by bounding box. In addition, a dual-view prohibited item feature fusion and detection framework in X-ray images is proposed, where the two input channels are applied and divided into primary and secondary channels, and the features of the secondary channel are used to enhance the features of the primary channel through the feature fusion model. Spatial attention and channel attention are employed to achieve efficient feature screening. We conduct some experiments to verify the effectiveness of the proposed dual-view prohibited item detection framework in X-ray images. The Dualray dataset and dual-view object detection code are available at https://github.com/zhg-SZPT/Dualray.
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Acknowledgments
This work was supported in part by Shenzhen Science and Technology Program (No. RCBS20200714114940262), and in part by General Higher Education Project of Guangdong Provincial Education Department (No. 2020ZDZX3082), and in part by Stable Supporting Programme for Universities of Shenzhen (No. 20200825181232001).
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Wu, M., Yi, F., Zhang, H., Ouyang, X., Yang, J. (2022). Dualray: Dual-View X-ray Security Inspection Benchmark and Fusion Detection Framework. In: Yu, S., et al. Pattern Recognition and Computer Vision. PRCV 2022. Lecture Notes in Computer Science, vol 13537. Springer, Cham. https://doi.org/10.1007/978-3-031-18916-6_57
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