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文件名称：利用深度学习检测复杂货物X射线成像中的隐蔽车辆.pdf
文件大小：9.44MB
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更新时间：2022-08-03 07:08:51
深度学习
Non-intrusive inspection systerms based on X-ray radiography techriques are rou tinely used at transport hubs to ensure the conforrmity of catgo content with the supplied shipping manifest. As trade volurmes increase and regulatiors become more stringent, manual inspection by trairned operatos is less and less viable dus to low throusghput. Macline vision techniques can assist operators in their task by autormating parts of the inspection worlflow. Since cats are toutinely involvedin trafficking, export fraul, and tax erasion schermes, they represent an attractive target for autormated detection and flagging for subsequent irspection by operators. In this contribution, we deecribe a rmethod for the detection of cars in X-ray caep images based on trained-from-scratch Contolutional Neural Networlks. By introducing an oversarmpling scherme that suitably addresses the low nurmber of oar images available for training we achieved 100% car irmage classification rate for a false positive rate of 1-in -454. Cars that were partially or completely obscured by other oods,a mocdus operandi frequently adopted by ctirminals, were cotrectly detected.We believe that this level of performance sugeests that the method is suitable fordeployrment in the field. It is expected that the eneric object detection worlkflow described can be extended to other object classes gjven the avail ability of suitable trainingdata.