行李安检智能检测最难分样本集选取方法研究

An approach to select hardest sample set for intelligent detection in baggage security

研究表明,使业务模型性能变差的难分样本对系统边界决策能力增长有决定性影响。由于行李携带危险品的多样性及实物形态的不确定性,以及现场行李安检系统生成的行李X光图像数据呈现“长尾分布”特征,由有限次样本采集的数据集训练得到的智能检测模型,在应用于现场行李安检系统后,存在检测准确率不高的问题。文章针对行李安检智能检测数据分析闭环流程,提出最难分样本集的离散强化选取方法,可从现场行李安检系统运行过程中产生的危险品实例图像中选取最难分样本集,作为新增样本数据,用于智能检测模型的学习更新,实现安检智能检测软件性能的持续增强。

It is shown that hard samples worsening the performance of a system model has a decisive impact on the growth in the system's boundary decision capability.Due to the diversity of dangerous goods and the uncertainty of their instance forms as welll as the feature of"long-tail distribution"of the baggage X-ray image data generated by the on-site security system,the intelligent baggage detection model,which is trained based on the data set of finite sample collection,has the problem of low detection accuracy when applied to the on-site baggage security system.In this paper,we analyze the closed-loop data analysis process of the intelligent baggage security check,and propose a discrete reinforcement search method for selecting the hardest sample set from the X-ray images of dangerous goods instances generated during the operation of the baggage security system.Then,the hardest sample set is used as new training dataset to complete the learning update of the intelligent baggage detection model,thus realizing the continuous growth of the performance of the intelligent baggage security check software.