基于图匹配网络的小样本违禁物品分割算法

Few-shot prohibited item segmentation algorithm based on graph matching network

自动化安检技术是维护公共安全、提升安检效率的一项有效措施。在实际场景中很难获得充足的违禁品标注样本用于神经网络的训练,并且在不同场景和安全级别下违禁品的类别也有所不同。为解决基于神经网络的违禁品检测方法所面临的样本不均衡问题,以及避免模型在分割新的违禁品类别时需重新训练的现象,文中提出一种基于图匹配网络的小样本违禁物品分割算法。文中模型将测试图像与参考图像并行输入到图匹配网络中,并根据匹配结果从测试图像中分割出违禁品。所设计的图匹配模块不仅从图间节点的相似性考虑匹配问题,并利用DeepEMD算法建立全局概念,进一步提高测试图和参考图的匹配结果。在SIXray数据集和Xray-PI数据集上的实验表明:本模型在单样本分割任务中得到36.4%和51.2%的类平均交并比,分别比目前先进的单样本分割方法提高2.5%和2.3%。由此表明所设计的算法能有效提升小样本X光图像分割算法的精确度。

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.