• Infrared and Laser Engineering
  • Vol. 50, Issue 11, 20210075 (2021)
Zhenyue Zhu1, Shujing Lv1、2、*, and Yue Lv1、2
Author Affiliations
  • 1Department of Computer Science and Technology, East China Normal University, Shanghai 200062, China
  • 2Shanghai Key Laboratory of Multidimensional Information Processing, Shanghai 200241, China
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    DOI: 10.3788/IRLA20210075 Cite this Article
    Zhenyue Zhu, Shujing Lv, Yue Lv. Few-shot prohibited item segmentation algorithm based on graph matching network[J]. Infrared and Laser Engineering, 2021, 50(11): 20210075 Copy Citation Text show less

    Abstract

    Automated security inspection is an effective measure to maintain public safety and improve the efficiency of security inspection. Usually, it is difficult to obtain enough labelled samples which contain some prohibited items of rarely appearing. Furthermore, the category of prohibited items varies in different scenarios and security levels. A graph matching network algorthm for few-shot prohibited item segmentation was introduced to deal with the imbalance of training samples faced by neural network methods, and to inspect prohibited items of new categories without the requirement of retraining. This model parallelly input a query image and several support images into the graph matching network, and segmented the prohibited items from the query image according to the matching results. The graph matching module not only considered the matching problem from the point of node similarity between two graphs, but also establisheed a global concept to match the graphs with the use of DeepEMD algorithm. Experiments on the SIXray dataset and Xray-PI dataset show that proposed model achieves 36.4% and 51.2% meanIoU for 1-shot tasks and outperforms the state-of-the-art method by 2.5% and 2.3% meanIoU, respectively. The extended experiments demonstrate that propoed algorithm can effectively improve the accuracy of few-shot X-ray image segmentation.
    Zhenyue Zhu, Shujing Lv, Yue Lv. Few-shot prohibited item segmentation algorithm based on graph matching network[J]. Infrared and Laser Engineering, 2021, 50(11): 20210075
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