基于改进YOLOv5s的X光图像危险品检测  

X-ray image dangerous goods detection based on improved YOLOv5s

作　　者：张康佳 张鹏伟[1] 陈景霞[1] 龙闵翔 林文涛 ZHANG Kang-jia;ZHANG Peng-wei;CHEN Jing-xia;LONG Min-xiang;LIN Wen-tao(School of Electronic Information and Artificial Intelligence,Shaanxi University of Science&Technology,Xi′an 710021,China)

机构地区：[1]陕西科技大学电子信息与人工智能学院,陕西西安710021

出　　处：《陕西科技大学学报》2023年第6期176-183,200,共9页Journal of Shaanxi University of Science & Technology

基　　金：国家自然科学基金项目(61806118);陕西科技大学博士科研启动基金项目(2020BJ-30)。

摘　　要：随着近年来交通系统越来越发达,人们出行越来越频繁,通过X光安检机对人们的包裹进行检查,已经成为预防危及公共安全事件的重要手段.目前,很多地方X光图像的危险品检测工作仍然由安检员人工进行,存在工作负荷大、效率低等问题.因此,利用现有的目标检测技术自动进行危险品检测非常必要.但X光图像危险品背景往往比较复杂,导致危险品自动检测精度不高.因此,本文提出一种改进的att_decouple_YOLOv5s模型,通过在YOLOv5s模型的特征融合部分引入卷积注意力机制(Convolutional Block Attention Module,CBAM),以加强相关特征信息、抑制背景信息;对模型的检测头部通过并行分支的方法进行解耦,解决分类和定位任务因为耦合在一起所产生的冲突问题.最后,在公共数据集pidray进行X光图像12类危险品检测实验,实验结果表明,所提模型的检测性能指标均值平均精度在IoU(Intersection over Union)阈值为0.5的情况下达到了88.1%,相比于YOLOv5s模型提升了2.8%;在IoU阈值为0.5到0.95的情况下,均值平均精度达到了76.6%,相比于YOLOv5s提升了4.3%;验证了本文改进算法的有效性.In recent years,as the transportation system becomes more and more developed and people travel more and more frequently,it has become an important means to check people′s packages through X-ray security screening machines to prevent incidents endangering public safety.At present,dangerous goods detection of X-ray images in many places is still carried out manually by security inspectors,which has problems of heavy workload and low efficiency.Therefore,it is necessary to use the existing target detection technology to detect dangerous goods automatically.However,the background of dangerous goods in X-ray images is often complicated,which leads to the low accuracy of the automatic detection of dangerous goods.Therefore,this paper proposes an improved att_decouple_YOLOv5s model.By introducing the Convolutional Block Attention Module(CBAM)in the feature fusion part of YOLOv5s model,the relevant feature information is strengthened and the background information is suppressed;The detection head of the model is decoupled by the parallel branch method to solve the conflict problem caused by the coupling of classification and positioning tasks.Finally,this paper tested 12 kinds of dangerous goods in X-ray images in the public data set pidray.The experimental results showed that the average accuracy of the detection performance index of the proposed model reached 88.1%when the IoU(Intersection over Union)threshold was 0.5,which was 2.8%higher than that of the YOLOv5s model;When the IoU threshold was between 0.5 and 0.95,the average precision reached 76.6%,which was 4.3%higher than that of YOLOv5s.The effectiveness of the improved algorithm is fully verified.

关 键 词：X光图像 危险品检测 目标检测 YOLOv5s CBAM 解耦 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]