测试技术学报2019，Vol.33Issue(1)：48-53,6.DOI:10.3969/j.issn.1671-7449.2019.01.009
基于增量学习的X射线安检系统检测算法研究
Detection Algorithm of X-ray Security Inspection System Based on Incremental Learning
摘要
Abstract
A target detection algorithm for X-ray security inspection system based on incremental learning was studied aiming at problems that the existing intelligent X-ray security inspection system can't effectively detect heterogeneous dangerous objects which emerges newly, and retraining is inefficient.In the method, the feature extractor of faster rcnn which was in traditional target detection network was replaced by the residual network, and an incremental learning network was constructed of Target Detection by adding target classification and border regression neurons corresponding new classes in the last fully connected layer of the network, and that distillation loss was introduced in the loss function of the incremental network to solve the catastrophic forgetting problem which was caused by updating the network with only new data.Finally, based on the original 7-class data training model of the X-ray security system, one class of new target data is sequentially added to continue training and detection, and recognition rate of the new target is not less than 90％.The experimental results show that the algorithm can detect new dangerous object with high precision while maintaining the detection ability of old classes.关键词
增量学习/目标检测/安检系统/损失函数Key words
increment learning/object detection/security inspection system/loss function分类
信息技术与安全科学引用本文复制引用
田敏皓,陈平..基于增量学习的X射线安检系统检测算法研究[J].测试技术学报,2019,33(1):48-53,6.基金项目
国家自然科学基金资助项目(61571404,61871351,61801437) （61571404,61871351,61801437）
