摘要
针对远距离红外目标探测技术中存在的弱小目标特征信息提取困难、局部背景噪声干扰强导致检测算法虚警率和漏检率偏高的问题,提出了融合CNN-Transformer的单帧红外弱小目标检测算法。针对已有方法在提取红外弱小目标特征信息时感受野受限且易受到局部噪声干扰的问题,对Swin Transformer中的窗口自注意力计算模块进行改进,设计了基于可分离卷积的局部感知增强模块,兼顾对全局信息和局部信息的提取,提升骨干网络对弱小目标空间分布信息的提取能力。针对小目标特征难以在深层网络中保留的问题,设计了自下而上的多尺度特征融合模块,在不同层级的特征图之间利用注意力机制确保小目标的低层特征信息能够在高层特征图中得以保留。在公开数据集NUAA-SIRST上进行了测试,验证了本文所提算法相比已有算法取得了更佳的检测效果,同时能够兼顾对检测精度和召回率的优化。
Aiming at the problems that the feature information extraction of dim and small targets is difficult in the remote infrared target detection technology,and the strong noise interference leads to the high false alarm rate and missing detection rate of the detection algorithm,a single-frame infrared dim and small target detection algorithm based on convolutional neural network(CNN)-Transformer is proposed.Aiming at the problem that the receptive field is limited and vulnerable to local noise interference when extracting the feature information of infrared dim and small targets with the existing methods,the window self-attention calculation module in Swin Transformer is improved,and a local perception enhancement module based on separable convolution is designed,which takes into account the extraction of global information and local information,and improves the ability of backbone network to extract the spatial distribution information of small and weak targets.Aiming at the problem that small target features are difficult to retain in the deep layer,a bottom-up multi-scale feature fusion module is designed to ensure that the low-level information of the small target can be retained in the high-level feature map with the attention mechanism between the feature maps at different levels.The test results on the public dataset NUAA-SIRST verify that the algorithm proposed in this paper has achieved better detection performance compared with the existing algorithms,while taking into account the optimization of accuracy and recall.
作者
李建
丁乐琪
王碧云
蔡云泽
LI Jian;DING Leqi;WANG Biyun;CAI Yunze(Shanghai Aerospace Control Technology Institute,Shanghai 201109;Infrared Detection Technology Research&Development Center of CASC,Shanghai 201109;Department of Automation,Shanghai Jiao Tong University,Shanghai 200240;National Key Laboratory of Air-based Information Perception and Fusion,Luoyang 471009)
出处
《飞控与探测》
2024年第2期62-72,共11页
Flight Control & Detection
基金
中国航天科技集团有限公司第八研究院产学研合作基金项目(USCAST2021-5)。
