降质靶标检测算法  

Degrad Target Detection Algorithm

作　　者：刘鹏[1] 熊泽宇 景文博[2] 冯萱 张俊豪 刘桐伯 吴雪妮 夏璇 万琳琳 赵海丽[1] LIU Peng;XIONG Zeyu;JING Wenbo;FENG Xuan;ZHANG Junhao;LIU Tongbo;WU Xueni;XIA Xuan;WAN Linlin;ZHAO Haili(School of Electronic and Information Engineering,Changchun University of Science and Technology,Changchun 130000,Jilin,China;School of Opto-Electronic Engineering,Changchun University of Science and Technology,Changchun 130000,Jilin,China)

机构地区：[1]长春理工大学电子信息工程学院,吉林长春130000 [2]长春理工大学光电信息工程学院,吉林长春130000

出　　处：《兵工学报》2024年第6期2065-2075,共11页Acta Armamentarii

基　　金：吉林省科技发展计划项目(20210201092GX)。

摘　　要：装甲车辆动态性能考核中的立靶成像测试环节,靶标检测的准确性与武器装备鉴定及定型的精度息息相关。针对靶标图像对比度低、可辨识度低等降质问题,提出一种基于改进YOLOv5的降质靶标检测算法:使用多分支分组卷积结构配合深度、逐点卷积搭建主干特征提取网络,降低网络参数计算量,提高网络的检测速度;引入表征注意力机制,增强靶标的表征能力;在网络输出层,引入3分支空间特征融合,利用低层特征图的细粒度特征信息与高层特征图丰富的语义信息组合,保留降质靶标图像的细节、边缘语义信息;实验结果表明:在靶标数据集中,所提算法的检测精度mAP达到90.88%,检测速度达到52.74帧/s,能在降质环境下够高效、精准地完成动态性能考核中立靶成像测试环节中的靶标检测部分。The target detection accuracy in the imaging test phase of armored vehicle dynamic performance assessment is closely related to the precision of weapon equipment identification and qualification.To address the degradation issues such as low target image contrast and poor discernibility,a degraded target detection algorithm based on improved YOLOv5 is proposed.The proposed algorithm utilizes a multi-branch grouping convolutional structure combined with deep and pointwise convolutions to construct a backbone feature extraction network,thus reducing the computational complexity of network parameters and improving the detection speed.The representation attention mechanism is introduced to enhance the representation capability of the targets.At the network output layer,a three-branch spatial feature fusion is introduced to combine the fine-grained feature information from low-level feature maps and the rich semantic information from high-level feature maps,preserving the details and edge semantic information of degraded target images.Experimental results demonstrate that,in the target dataset,the proposed algorithm achieves a detection accuracy of 90.88%in terms of mean average precision(mAP)and a detection speed of 52.74 fps.It can efficiently and accurately complete the target detection phase in the imaging test of dynamic performance assessment.

关 键 词：靶标 降质图像 目标检测 特征融合 注意力机制 

分 类 号：TJ811[兵器科学与技术—武器系统与运用工程]