基于数据驱动的红外弱小机动目标检测与跟踪  

Data-Driven Infrared Weak Maneuvering Target Detection and Tracking

作　　者：陈华杰[1] 钱逸凡 龙翔 许啸 吴浩宇 CHEN Huajie;QIAN Yifan;LONG Xiang;XU Xiao;WU Haoyu(School of Automation,Hangzhou Dianzi University,Hangzhou 310000,China)

机构地区：[1]杭州电子科技大学自动化学院,杭州310000

出　　处：《电光与控制》2024年第12期64-71,105,共9页Electronics Optics & Control

基　　金：浙江省科技计划项目(2022C01095)。

摘　　要：针对复杂背景下红外弱小目标检测跟踪中目标特征缺失、强杂波干扰等问题,基于多帧的处理方法引入了目标运动特征来提升性能,然而针对无人机等新型强机动目标,现有的基于机理模型的多帧处理方法难以覆盖其复杂多变的运动形态,导致其检测跟踪的效果并不理想。对此,以多帧的处理方法为基础框架,提出了一种复杂背景下基于数据驱动的红外弱小机动目标检测跟踪方法:首先采用MPCM算法对弱小目标进行增强;其次将多帧的增强结果投影到二维子空间,构建基于YOLO的二维轨迹检测模型;最后将二维检测结果进行三维时空回溯,构建基于LSTM的三维轨迹检测模型。构建检测模型时,在实测图像的基础上进行了数据增广,使得训练样本尽量覆盖目标的各种运动形态;在二维子空间上,利用高性能YOLO检测网络,快速剔除大量杂波;在三维时空上,利用LSTM时序网络对少量难剔除的杂波进行精细化筛除。与多种算法的对比实验结果表明,所提方法在耗时上能满足实时性要求,同时,在多个场景下的平均跟踪精度能达到86.6%,虚警率仅有9.2%,ROC曲线下的AUC值能达到0.9820。To solve the problems of missing target features and strong clutter interference in infrared weak target detection and tracking in complex backgrounds,the target motion feature is introduced into the multi-frame processing method to improve the performance.However,for highly maneuverable targets such as UAVs,the existing multi-frame processing methods based on mechanism models make it difficult to cover their complex and variable motion forms,which makes the result of detection and tracking unsatisfactory.In this regard,a data-driven infrared weak maneuvering target detection and tracking method under complex backgrounds based on a multi-frame processing framework is proposed.Firstly,the MPCM algorithm is used to enhance weak targets.Then,the enhanced results of multiple frames are projected onto a 2D subspace to construct a 2D trajectory detection model based on YOLO.Finally,the 2D detection results are backtracked in 3D space-time to construct a 3D trajectory detection model based on LSTM.When constructing the detection model,data augmentation is performed on the images of real image to ensure that the training samples cover as many target motion forms as possible.In the 2D subspace,a high,performance YOLO detection network is used to quickly eliminate a large amount of clutter.In the 3D space-time,a small amount of difficult-to-eliminate clutter is finely screened out by the LSTM temporal network.The results of comparison experiment demonstrate that the proposed method is capable of achieving real-time performance in terms of time consumption,and the method exhibits an average tracking accuracy of 86.6%across multiple scenarios,a remarkably low false alarm rate of only 9.2%,and an impressive AUC value of 0.9820 for the ROC curve.

关 键 词：红外弱小目标 复杂背景 强机动目标 数据驱动 二维子空间 三维时空 

分 类 号：TP273[自动化与计算机技术—检测技术与自动化装置]