# SAR Image Aircraft Target Recognition Based on Improved YOLOv5
## 3 Materials and Methods
### Design of the Ghost-Conv and SE-Ghost-C3 Lightweight Modules  
The deep convolution neural network is composed of a large number of convolutions, which leads to large computational costs [31]. There are many redundant feature maps in the calculation process. This paper proposes the Ghost-Conv and SE-Ghost-C3 modules, which are capable of extracting features efficiently with a small amount of computational complexity.  
C1 and C2 represent the dimensions of the feature maps. The core module is Ghost-Conv, which is shown in Figure 2a. The first convolution layer is used to generate intrinsic feature maps. The dimension of the output channel is half of C2. The other half of the dimension is obtained via a cheap operation. We adopted the depthwise (DW) convolution [32,33] operation to generate the other half dimension, which were set as redundant features. They were concatenated together as the output of the C2 dimension of Ghost-Conv. Under the same conditions, the computational cost of Ghost-Conv was reduced by one time compared with ordinary convolution. Ghost-Conv replaced ordinary Conv of the CBS module in the backbone and neck of YOLOv5. However, the settings of the stride, kernel size, and the number of input and output channels remained unchanged. The size of the model was greatly reduced.  
To further compress the model, we designed the SE-Ghost-C3 module, which is shown in Figure 2c. It is composed of the SE-Ghost-Bottleneck module, which is shown in Figure 2b. The SE-Ghost-Bottleneck module consists of two stacked Ghost-Conv modules. The first Ghost-Conv module acts as a squeezing layer, reducing the number of channels. The second Ghost-Conv module recovers the number of channels to match the shortcut path. _exp_ denotes the squeezing rate, which was set as 0.5 in our experiments. The SE attention mechanism is integrated between the two Ghost-Conv modules, which enables the feature map with more information to obtain a greater weight. This helps improve the efficiency of feature extraction. There are two (b) in Figure 2, which are distinguished by the bottleneck flag bit. When the bottleneck is true, SE-Ghost-Bottleneck configures the residual structure, which can increase the capacity to transmit the features in the network from shallow to deep layers and effectively alleviate the gradient-vanishing problem caused by ordinary convolution. The SE-Ghost-C3 module was used as the backbone of YOLOv5. When the bottleneck is false, SE-Ghost-Bottleneck does not configure the residual structure, which is used as the neck of YOLOv5. The SE-Ghost-C3 module can better fuse multiscale features. We designed the SE-Ghost-C3 module on the basis of the cross-stage partial network (CSPNet) [34]. The stacking of multiple bottleneck layers incurs great overhead costs. We adopted SE-Ghost-Bottleneck to replace the original bottleneck. This reduced the network operations and improved the efficiency of feature extraction. SE-Ghost-C3 enables the network to learn useful information, discard useless information, and effectively reduce the number of parameters without reducing the performance.