优化YOLOv4算法的安检X光图像检测网络  

X-Ray Security Image Detection Network Based on Optimized YOLOv4

作　　者：杨嘉诚 黄佳慧 韩永麟 王萍[1] 李晓辉[1] YANG Jia-Cheng;HUANG Jia-Hui;HAN Yong-Lin;WANG Ping;LI Xiao-Hui(School of Electronics and Control Engineering,Chang’an University,Xi’an 710021,China)

机构地区：[1]长安大学电子与控制工程学院,西安710021

出　　处：《计算机系统应用》2021年第12期116-122,共7页Computer Systems & Applications

摘　　要：针对目前安检场景中违禁品种类复杂、人工检查效率低易出错等问题,文章提出一种名为Res152-YOLO的网络架构,该架构基于YOLOv4(You Only Look Once)优化目标检测网络.为提高对X光图像中危险品的检测精度,Res152-YOLO采用ResNet-152网络代替原YOLOv4中的CSPDarknet-53网络,将改进后的ResNet残差网络与YOLOv4网络连接.实验中利用YOLOv4、Res152-YOLO等一系列网络在同一数据集上进行对比实验,分别比较上述网络的损失曲线、对各类危险品的检测性能以及各网络的总体性能.结果表明,Res152-YOLO网络在以上实验中性能优于原YOLOv4网络,并且满足安检设备的帧率要求.改进后的网络有效提高了安检的准确率,能够消除一定的安全隐患.Given the diverse prohibited varieties in current security inspection scenes and low-efficiency error-prone manual inspections,this study proposes network architecture,Res152-YOLO,based on the YOLOv4 optimized target detection network.Res152-YOLO uses the ResNet-152 network to replace the CSPDarknet-53 network in the original YOLOv4 and connects the improved ResNet to the YOLOv4 network to enhance the detection accuracy of dangerous goods in X-ray images.A series of networks such as YOLOv4 and Res152-YOLO are used to conduct comparative experiments on the same data set to compare the loss curves of the above-mentioned networks,the detection results for various dangerous goods,and the overall performance of each network.The results show that the improved network can improve the accuracy of security detection and eliminate potential safety hazards.

关 键 词：YOLOv4 残差网络 深度学习 卷积神经网络 数据增强 安检X光 违禁品识别 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术] TP18[自动化与计算机技术—计算机科学与技术]