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兵工学报 ›› 2021, Vol. 42 ›› Issue (12): 2675-2683.doi: 10.3969/j.issn.1000-1093.2021.12.015

• 论文 • 上一篇    下一篇

基于改进YOLOv4的X射线图像违禁品检测算法

穆思奇, 林进健, 汪海泉, 魏雄志   

  1. (武警警官学院 训练基地, 广东 广州 510440)
  • 发布日期:2022-01-15
  • 通讯作者: 林进健(1993—),男,讲师,硕士 E-mail:dgszgdut@163.com
  • 作者简介:穆思奇(1969—),男,教授。

An Algorithm for Detection of Prohibited Items in X-ray Images Based on Improved YOLOv4

MU Siqi, LIN Jinjian, WANG Haiquan, WEI Xiongzhi   

  1. (School of Training Base, Police Officers College of PAP, Guangzhou 510440, Guangdong, China)
  • Published:2022-01-15

摘要: 为提高安检速度、实现X射线图像中违禁物品的自动检测,提出一种基于改进YOLOv4的X射线图像违禁品检测算法。该算法在单阶段目标检测算法YOLOv4基础上设计一种空洞密集卷积模块。将上采样链路融合后特征输入空洞密集卷积模块中,增强特征表达能力和卷积视野。对融合后特征信息加入注意力机制,用来增强有效特征和抑制无效特征,最终得到表征图像信息的特征图输入检测头部。采用Mosaic数据增强方法训练网络,提升网络的鲁棒性。结果表明:该算法在公开SIXray数据集上的均值平均精度达到80.16%,检测速度为25帧/s;该算法在公开SIXray数据集上多类违禁物品能够取得较高的检测精度,且满足检测的实时性要求。

关键词: 违禁品检测, YOLOv4, X射线图像, 空洞密集卷积, 注意力机制, 数据增强

Abstract: An improved YOLOv4 algorithm for detecting the prohibited items in X-ray images is proposed to increase the speed of security inspection and realize the automatic detection of prohibited items in X-ray images. The proposed algorithm is used to design a dilated dense convolution module based on the one-stage object detection algorithm YOLOv4. The features after the upsampling link fusion are input into the dilated dense convolution module to enhance the feature expression ability and the convolution field of vision. An attention mechanism is added to the fused feature information to enhance effective features and suppress invalid features. Finally,a feature map representing image information is input to detection head. Mosaic data enhancement method is used to train the network to improve the robustness of the network. The results show that the mean average precision (mAP) of the proposed algorithm on the public SIXray data set reaches 80.16%,and the detection speed is 25 frames per second (FPS). The proposed algorithm can achieve high detection accuracy for multiple types of prohibited items on the public SIXray dataset, and meet the real-time requirements of detection.

Key words: prohibiteditemsdetection, YOLOv4, X-rayimage, dilateddenseconvolution, attentionmechanism, dataaugmentation

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