# Aircraft Target Interpretation Based on SAR Images
## 3 Method
### Multi-Scale Receptive Field and Channel Attention Fusion (MRFCA)  
There are significant differences in the size of aircraft targets in our datasets, and the YOLOv5s algorithm has a weak feature extraction ability for multi-scale targets, especially in the case of complex backgrounds and lots of noise. Moreover, the YOLOv5s algorithm is more likely to miss small aircraft targets in the detection and recognition process. This paper proposed an MRFCA module, which combines the inception network and channel attention mechanism. It has better stability of detection and recognition for the targets with different scales, and it can adaptively adjust the weights of different receptive fields according to the input information.  
As the expression of Section 2.3, the inception network can expand the width of the network to achieve better performance. We proposed to create three branches to extract the feature information of different receptive fields. In order to improve the calculation speed and reduce the number of parameters, we adopted the dilated convolution method [34].  
Figure 3: Inception network.  
However, after the training process is completed, the parameters of the inception network are fixed, and the weights of different receptive fields are not dynamically adjusted. As the expression of Section 2.2, the SENet attention mechanism can improve the expression ability of the feature map. This paper proposes to incorporate the SENet network into our improved inception network. It not only considers the relationship between channels but also considers the importance of different receptive fields. With different image inputs, the weights are dynamically adjusted. The MRFCA network is shown in Figure 4. It contains split, fuse, and select.  
Where \(X\in R\) (\(h\times w\times c\)) is the input feature map, \(X\) is split into 3 branches \(U1\), \(U2\), and \(U3\), which are separately convolved operations with kernels of different sizes. \(U1\in R\) (\(h\times w\times c\)) is the convolution of \(X\) and kernel (\(3\times 3\)), \(U2\in R\) (\(h\times w\times c\)) is the convolution of \(X\) and kernel (\(5\times 5\)), \(U3\in R\) (\(h\times w\times c\)) is the convolution of \(X\) and kernel (\(7\times 7\)). The kernel (\(3\times 3\)), kernel (\(5\times 5\)) and kernel (\(7\times 7\)) adopt the dilated convolution method. The fuse operation is an element-wise summation of \(U1\), \(U2\), and \(U3\), \(U\in R\) (\(h\times w\times c\)) is the feature map of fusion, it contains the feature information with different receptive fields, and the operation is expressed as:  
\[U=U1+U2+U3 \tag{4}\]  
where \(S\in R\) (\(1\times 1\times c\)) is the feature vector of the global average pooling operation at the dimensions \(w\) and \(h\) of \(U\); the dimension of each channel is composed into one pixel, which has a global receptive field of each channel. To further improve the efficiency of the network and reduce the number of parameters, \(S\) carries out cross-channel information interaction through the FC operation to obtain \(Z\in R\) (\(1\times 1\times z\)), the dimension is squeezed to \(z\). At select section, \(Z\) performs three FC operations, respectively, to obtain three new feature vectors of dimension \(c\), then performs softmax function to predict the weights of the different receptive fields, the weight vectors {\(a\), \(b\), \(c\)\(R\) (\(1\times 1\times c\))} are expressed as follows:  
\[a=\frac{e^{AZ}}{e^{AZ}+e^{BZ}+e^{CZ}},\ b=\frac{e^{BZ}}{e^{AZ}+e^{BZ}+e^{CZ}}, \ c=\frac{e^{CZ}}{e^{AZ}+e^{BZ}+e^{CZ}} \tag{5}\]  
where \(e\) is the natural logarithm; \(A\), \(B\), and \(C\in R\) (\(c\times z\)) is a two-dimensional operational matrix, whose values are constantly corrected as the input image changes. The feature map of final output \(V\) is expressed as follows:  
\[V=V1+V2+V3=aU1+bU2+cU3 \tag{6}\]  
Figure 4: MRFCA network.  
MRFCA is a plug-and-play module integrated behind the first C2 layer in the backbone of YOLOv5s. It can improve the feature extraction ability of YOLOv5s for multi-scale targets.