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An automated detection model of threat objects for X-ray baggage inspection based on depthwise separable convolution

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Abstract

X-ray baggage inspection is an essential task to detect threat objects at important controlled access places, which can guard personal safety and prevent crime. Generally, it is carried out by screeners to visually determine whether or not a bag contains threat objects. Whereas, manual detection exhibits distinct shortcomings, from high detection errors to different detection results produced by screeners. These limitations can be addressed by introducing automated detection model of threat objects for X-ray baggage inspection. However, existing automated detection methods cannot realize end-to-end detection and the detection results include only classification without location. In this paper, we propose an automated detection model of threat objects based on depthwise separable convolution. Our model is able to not only categorize the threat object but also locate it simultaneously. The network model has the advantage of high detection accuracy, fast computational speed, and a few parameters. Meanwhile, the precision of threat object regions is enhanced with the help of multi-scale prediction. A deformation layer is added in our model, which can provide invariance to affine warping. The experiments on the GDXray database (Mery et al. in J Nondestr Eval 34(4):42, 2015) demonstrate that the overall performance of our proposed model is superior to YOLOv3 (Redmon J and Farhadi A in YOLOv3: an incremental improvement, 2018) model, SSD (Liu et al. in SSD: single shot multibox detector. In: European Conference on Computer Vision (ECCV), pp. 21–37, 2016) model, and Tiny_YOLO (Redmon et al. in You only look once: unified, real-time object detection. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 779–788, 2015) model.

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Acknowledgements

This research is supported by the National Natural Science Foundation of China (Grant Nos. 61374178, 61402092, 61603182), the Fundamental Research Funds for the Central Universities (N171704004), the online education research fund of MOE research center for online education, China (Qtone education, Grant No.2016ZD306), and the Ph.D. Start-Up Foundation of Liaoning Province, China (Grant No. 201501141).

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  1. Software Collage, Northeastern University, Shengyang, 110819, China

    Yiru Wei, Zhiliang Zhu, Hai Yu & Wei Zhang

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  1. Yiru Wei
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  2. Zhiliang Zhu
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  4. Wei Zhang
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Correspondence to Zhiliang Zhu.

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Wei, Y., Zhu, Z., Yu, H. et al. An automated detection model of threat objects for X-ray baggage inspection based on depthwise separable convolution. J Real-Time Image Proc 18, 923–935 (2021). https://doi.org/10.1007/s11554-020-01051-1

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