基于改进YOLOv8s的X射线安检图像违禁品检测方法
Prohibited Item Detection Method of X-Ray Security Inspection Image Based on Improved YOLOv8s
董佳鑫 1罗婷 1李根 1赵星 2赵云松2
作者信息
- 1. 中国人民公安大学信息网络安全学院,北京 100038
- 2. 首都师范大学数学科学学院,北京 100048;首都师范大学检测成像北京市高等学校工程研究中心,北京 100048
- 折叠
摘要
针对违禁品在X射线安检图像中存在漏检、误检的问题,在YOLOv8s模型的基础上提出一种引入动态卷积模块、加权双向特征金字塔网络(BiFPN),以及全局注意力机制的改进模型——YOLOv8s-BiOG.改进模型将骨干网络与颈部网络中的部分卷积模块替换为动态卷积模块,细化违禁品局部特征,增强特征提取能力.然后,用BiFPN对模型特征融合网络进行改进,优化模型处理不同尺度特征融合的能力.最后采用全局注意力机制,减少特征的丢失,增强违禁品检测性能.在SI2Pxray数据集和OPIXray数据集上的实验结果表明,改进模型对多种违禁品的检测精度有显著的提升.
Abstract
Addressing the challenges of missed and false detections of prohibited items in X-ray security imaging,this study introduces an enhanced model,termed YOLOv8s-BiOG.This model builds upon the foundational YOLOv8s framework by incorporating dynamic convolution module,weighted bidirectional feature pyramid network(BiFPN),and global attention mechanism.The dynamic convolution modules replace select convolutional components in both the backbone and neck networks,facilitating refined local feature analysis of prohibited items and bolstering feature extraction capabilities.Subsequently,the BiFPN enhances the model's feature fusion network,optimizing its proficiency in managing feature integration across various scales.The adoption of a global attention mechanism aims to mitigate feature loss and amplify the model's performance in detecting prohibited items.Experimental evaluations conducted on the SI2Pxray and OPIXray datasets demonstrate notable improvements in detection accuracy for a range of prohibited items.
关键词
违禁品检测/YOLOv8s/动态卷积/BiFPN/全局注意力机制Key words
prohibited item detection/YOLOv8s/dynamic convolution/BiFPN/global attention mechanism引用本文复制引用
出版年
2024
NETL
NSTL
万方数据