基于卷积神经网络与注意力机制的SAR图像飞机检测

Aircraft detection in SAR images based on convolutional neural network and attention mechanism

在合成孔径雷达(synthetic aperture radar,SAR)图像应用领域,对SAR图像中飞机目标的检测备受关注。针对现有检测算法模型运算复杂度高、检测性能较低的问题,提出一种基于深度可分离卷积神经网络与注意力机制的SAR图像飞机检测算法。首先使用深度可分离卷积神经网络提取图像特征,同时在网络中引入逆残差块,以有效防止通道数压缩引起的特征信息丢失问题;其次在网络中引入多尺度空洞卷积—空间注意力模块和全局上下文通道注意力模块,通过重新分配显著区域和各特征图更有代表性的权值,以更好地捕捉空间有效信息和通道间语义相关性,提高模型特征表达能力;最后在SAR飞机数据集(SAR aircraft dataset,SAD)上进行对比实验验证。实验结果表明,所提算法具有更好的检测效果,平均准确率达到86.3%,检测速度达到22.4 fps/s。

In the application field of synthetic aperture radar(SAR)images,the detection of aircraft target in SAR images has attracted much attention.Aiming at the problems of high computational complexity and low detection performance of existing detection algorithm models,an aircraft detection in SAR images algorithm based on depthwise separable convolutional neural network and attention mechanism is proposed.Firstly,the depthwise separable convolutional neural network is used to extract image features,and the inverted residual block is introduced into the network to effectively reduce the loss of feature information caused by channel slimming.Secondly,the multi-scale dilated convolution spatial attention mechanism module and global context channel attention mechanism module are introduced into the network.By redistributing more representative weights to salient regions and each feature map,the spatial effective information and semantic correlation between channels can be better captured,and the ability of feature expression can be enhanced.Finally,the comparative experimental verification is carried out on the SAR aircraft dataset(SAD).Experimental results show that this algorithm has a better detection effect,the average precision is 86.3%,and the detection speed is 22.4 fps/s.