摘要
近年来,对伪装目标进行精准识别的军事需求不断加大,使得伪装目标分割(COS)技术应运而生。由于伪装目标与背景的融合度较高,COS比传统的目标分割难度更大。为更加精准地分割出伪装目标,构建完备的军用伪装目标数据集(MiCOD),并提出一种基于人类视觉系统的COS网络—COSNet。COSNet由特征提取模块、聚焦放大模块、多尺度特征图融合模块3部分组成。针对性设计的聚焦放大模块包含关键点聚焦模块和感受野放大模块,关键点聚焦模块通过模拟人类注意力高度集中的观察过程减少虚警率,而感受野放大模块通过仿生人类视觉感受野机制以增大观测范围、提升分割精度。损失函数方面,依据聚焦放大模块设计了更适用于伪装目标识别的关键点区域加权感知损失,以给予伪装目标更高的关注度。大量定量和定性实验结果表明:在自建数据集MiCOD上,与其他目标分割模型对比,COSNet在8个评价指标上均达到最优效果,分割精度明显提升;当模拟真实的战场环境时,COSNet平均灵敏度Sen mean为0.622,平均特异度Spe mean为0.670,漏检率和虚警率均低于其他算法。
In recent years,the increasing military need for accurate identification of camouflaged objects has brought camouflaged object segmentation(COS)technology into existence.COS is more difficult than traditional object segmentation because of the high“integration”of camouflaged objects with the background.In order to segment the camouflaged objects more accurately,we first construct a complete military camouflaged object dataset(MiCOD),and then propose a human vision system-based camouflaged object segmentation network called COSNet.COSNet consists of three parts:featrue extraction module,focus and magnification module,and multi-scale feature fusion module.The focus and magnification module consists of two key serial modules,namely,the key point focus module and the receptive field magnification module.The key point focus module reduces the false alarm rate by simulating the observation process with high human attention,while the receptive field magnification module increases the observation range to improve the segmentation accuracy by imitating the human visual receptive field mechanism.As for the loss function,key point weighted perceptual loss is designed based on the focus and magnification module,which is more suitable for the recognition of camouflaged objects.A large number of quantitative and qualitative experiments on MiCOD demonstrate that COSNet achieves optimal results in eight evaluation metrics and significantly improves the segmentation accuracy.When simulating real battlefield environment,Sen mean is 0.622,Spe mean is 0.670,and the missed detection rate and false alarm rate are lower compared to other algorithms.
作者
蒋昕昊
蔡伟
张志利
姜波
杨志勇
王鑫
JIANG Xinhao;CAI Wei;ZHANG Zhili;JIANG Bo;YANG Zhiyong;WANG Xin(School of Missile Engineering,Rocket Force University of Engineering,Xi an 710025,Shaanxi,China)
出处
《兵工学报》
EI
CAS
CSCD
北大核心
2023年第5期1456-1468,共13页
Acta Armamentarii
基金
国防科技173计划技术领域基金项目(2021-JCJQ-JJ-0871)。
关键词
伪装目标分割
计算机视觉
图像分割
关键点
数据集
camouflaged object segmentation
computer vision
image segmentation
key point
dataset
