行李安检禁限带物品识别多标签图像分类算法  

Multi-label image classification model for identification of prohibited items in luggage security check

作　　者：胡本翼 彭凯贝 张驰[1] 吕晓军 刘跃虎[1] HU Benyi;PENG Kaibei;ZHANG Chi;LV Xiaojun;LIU Yuehu(College of Artificial Intelligence,Xi’an Jiaotong University,Xi’an 710049,China;Institute of Computing Technologies,China Academy of Railway Sciences Corporation Limited,Beijing 100081,China)

机构地区：[1]西安交通大学人工智能学院,西安710049 [2]中国铁道科学研究院集团有限公司电子计算技术研究所,北京100081

出　　处：《铁路计算机应用》2022年第10期16-21,共6页Railway Computer Application

基　　金：科学技术部科技创新2030—“新一代人工智能”重大项目(2018AAA0102504)。

摘　　要：有效识别禁限带物品的智能识别算法有助于降低安检人员劳动强度,提升旅客行李安检作业效率。文章采用图像多标签分类的深度卷积神经网络,通过引入图像注意力机制与动态元融合,能够在卷积前向传递过程中补充低层图像视觉线索,有效应对行李X光图像中物品影像混叠干扰及低分辨率特征混淆的问题,增强对细粒度特征的识别能力;同时,引入外部神经知识的元选择网络,实现网络多阶段预测的自适应融合,以避免权重偏置现象。实验结果表明,文章算法能够克服行李X光图像中影像混叠和物品尺度变化带来的禁限带物品识别困难,有效提高识别准确率。An intelligent identification algorithm for effective identification of prohibited items can help reduce the labor intensity of security personnel and improve the efficiency of passenger luggage security. We propose a deep convolutional neural network with multi-label image classification in which attention mechanism and dynamic metafusion architecture are adopted to complement low-level image cues during the forward progression of the convolution computing and can effectively cope with the interference of pixel aliasing and the confusion of low-resolution features in fine-grained X-ray image, thus enhancing the ability to recognize fine-grained features. Besides, the meta selection network guided by external neural knowledge is also adopted to achieve adaptive fusion of multi-stage prediction without weight bias. The experimental results show that the proposed algorithm can overcome the difficulty of identification of prohibited items caused by image aliasing and item scale variation in X-ray baggage images, and effectively improve the recognition accuracy.

关 键 词：行李安检 禁限带物品识别 行李X光图像 深度卷积神经网络 多标签分类 注意力机制 元融合 

分 类 号：U293.23[交通运输工程—交通运输规划与管理] TP39[交通运输工程—道路与铁道工程]