A Study on Cascade R-CNN-Based Dangerous Goods Detection Using X-Ray Image

作     者：Sang-Hyun Lee 

作者机构：Department of Computer EngineeringHonam UniversityGwangsanguGwangju 62399Korea 

出 版 物：《Computers, Materials & Continua》 (计算机、材料和连续体（英文）)

年 卷 期：2022年第73卷第11期

页      面：4245-4260页

核心收录：

学科分类：0711[理学-系统科学] 07[理学] 08[工学] 081101[工学-控制理论与控制工程] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 071102[理学-系统分析与集成] 081103[工学-系统工程] 

主　　题：Cascade R-CNN model faster R-CNN model X-ray screening equipment Res2Net supervised learning semi-supervised learning 

摘      要：X-ray inspection equipment is divided into small baggage inspection equipment and large cargo inspection *** the case of inspection using X-ray scanning equipment,it is possible to identify the contents of goods,unauthorized transport,or hidden goods in real-time by-passing cargo through X-rays without opening *** this paper,we propose a system for detecting dangerous objects in X-ray images using the Cascade Region-based Convolutional Neural Network(Cascade R-CNN)model,and the data used for learning consists of dangerous goods,storage media,firearms,and *** addition,to minimize the overfitting problem caused by the lack of data to be used for artificial intelligence(AI)training,data samples are increased by using the CP(copy-paste)algorithm on the existing *** also solves the data labeling problem by mixing supervised and semi-supervised *** four comparative models to be used in this study are Faster Regionbased Convolutional Neural Networks Residual2 Network-101(Faster R-CNN_Res2Net-101)supervised learning,Cascade R-CNN_Res2Net-101_supervised learning,Cascade Region-based Convolutional Neural Networks Composite Backbone Network V2(CBNetV2)Network-101(Cascade R-CNN_CBNetV2Net-101)_supervised learning,and Cascade RCNN_CBNetV2-101_semi-supervised learning which are then compared and *** a result of comparing the performance of the four models in this paper,in case of Cascade R-CNN_CBNetV2-101_semi-supervised learning,Average Precision(AP)(Intersection over Union(IoU)=0.5):0.7%,AP(IoU=0.75):1.0%than supervised learning,Recall:0.8%higher.