基于深度学习的X光图像智能审像系统
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引用本文:张积存1,2,费继友1,宋雪萍1,冯佳伟2.基于深度学习的X光图像智能审像系统[J].计算技术与自动化,2021,(2):125-130
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张积存1,2,费继友1,宋雪萍1,冯佳伟2 (1.大连交通大学 机械工程学院 辽宁 大连 1160282.东软集团(大连)有限公司 智能云警业务中心辽宁 大连 116085) 
中文摘要:针对X光安检机人工审核图片存在的效率低、误检和漏检等问题,设计并实现了一套基于Mask R-CNN算法的X光图片智能审像系统。实现了X光图像采集、数据汇聚、分析处理、违禁物品自动检测、数据存储等功能。通过分析比较,选择ResNet101作为BackBone训练网络,选取6000张X光图片作为样本,对刀、枪、液体瓶、手机、充电宝等五类违禁品进行标注。对训练参数优化调整,训练出违禁品的Mask R-CNN模型。在测试集上使用COCO评估方法,检出违禁品的平均精准率mAP50达到了0.83,明显高于Faster R-CNN、YOLOv3、SSD513等算法,具有实际工程应用价值。
中文关键词:深度学习  X光图像  目标检测  Mask R-CNN  违禁品检测
 
Intelligent X-ray Image Examination System Based on Deep Learning
Abstract:In order to solve the problems of low efficiency, false detection and missed inspection of X-ray security inspection machine, an intelligent X-ray image examination system based on Mask R-CNN algorithm is designed and implemented. The system has X-ray image acquisition,data aggregation,analysis and processing, automatic detection of prohibited items, data storage and other functions.Through analysis and comparision, ResNet101 is selected as the BackBone training network, and 6000 X-ray images are selected as samples to label five types of contraband such as knife, gun, liquid bottle, mobile phone and portable battery. The Mask R-CNN model of contraband is trained by optimizing the training parameters. Using COCO evaluation method in the test set, the average accuracy of detecting contraband is 0.83, which is significantly higher than Faster R-CNN、YOLOv3、SSD513 algorithm, which has practical engineering application value.
keywords:deep learning  X-ray image  object detection  Mask R-CNN  contraband detection
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