基于跨模态数据增强的红外时敏目标检测技术  

Infrared time-sensitive target detection technology based on cross-modal data augmentation

作　　者：王思宇 杨小冈 卢瑞涛 李清格 范继伟 朱正杰 Wang Siyu;Yang Xiaogang;Lu Ruitao;Li Qingge;Fan Jiwei;Zhu Zhengjie(Missile Engineering Institute,PLA Rocket Force University of Engineering,Xi'an 710025,China)

机构地区：[1]火箭军工程大学导弹工程学院,陕西西安710025

出　　处：《红外与激光工程》2023年第9期328-339,共12页Infrared and Laser Engineering

基　　金：国家自然科学基金项目(62276274);航空科学基金项目(201851U8012)。

摘　　要：目前红外时敏目标检测技术在无人巡航、精确打击、战场侦察等领域应用广泛,但有些高价值目标图像的获取难度高且成本昂贵。针对红外时敏目标图像数据匮乏、缺少用于训练的多场景多目标数据、检测效果不佳等问题,文中提出一种基于跨模态数据增强的红外时敏目标检测技术,跨模态数据增强方法为两阶段模型。首先在第一阶段通过基于CUT网络的模态转换模型将包含时敏目标的可见光图像转换为红外图像,其次在第二阶段模型中引入coordinate attention注意力机制,随机生成大量红外目标图像,实现了数据增强效果。最后提出一种基于SE模块和CBAM模块改进的Yolov5目标检测架构,实验结果表明,文中提出的Yolov5(CSP-A)目标检测技术与原网络相比,准确率提升了7.36%,召回率提升了5.43%,平均精度提升了2.74%。有效提高了红外时敏目标的检测精度,实现了红外时敏目标精确检测。Objective Infrared time-sensitive targets refer to infrared targets such as ships and aircraft,which have high military value and the opportunity of attack is limited by the time window.Infrared time-sensitive target detection technology is widely used in military and civilian fields such as unmanned cruise,precision strike,battlefield reconnaissance,etc.The target detection algorithm based on deep learning has made great progress in the field of target detection due to its powerful computing power,deep network structure and a large number of labeled data.However,the acquisition of some high-value target images is difficult and costly.Therefore,the infrared timesensitive target image data is scarce,and the multi-scene and multi-target data for training is lacking,which makes it difficult to ensure the detection effect.Based on this,this paper proposes an infrared time-sensitive target detection technology based on cross-modal data enhancement,which generates"new data"by processing the data,expands the infrared time-sensitive target data set,and improves the model detection accuracy and generalization ability.Methods We propose an infrared time-sensitive target detection technology based on cross-modal data enhancement.The cross-modal data enhancement method is a two-stage model(Fig.1).First,in the first stage,the visible light image containing time-sensitive targets is converted into infrared images through the mode conversion model based on the CUT network,and then the coordinate attention mechanism is introduced into the second stage model to randomly generate a large number of infrared target images,realizing the data enhancement effect.Finally,an improved Yolov5 target detection architecture based on SE module and CBAM module is proposed(Fig.3).Results and Discussions The proposed cross-modal infrared time-sensitive target data enhancement method combines the style migration model with the target generation model,and uses the visible light image data set to achieve infrared time-sensitive target data enhancem

关 键 词：红外时敏目标 数据增强 模态转换 目标检测 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]