## 3 Improved AEGG-FD Structure
### Attention-Enhanced Ghost Mode  
When a neural network is sufficiently well trained, the feature map contains rich or even redundant information to capture the valuable features of input data [45]. The feature maps after the first standard convolutional processing in YOLOv5 are shown in Figure 5. Reductions in computational complexity and the number of parameters can be achieved by replacing the SC operation with an inexpensive linear operation. Then, the redundant information can be effectively handled. The structure of the specific Ghost module is shown in the Figure 6, where three similar feature map pair examples are annotated with boxes of the same colour. One feature map in the pair can be approximately obtained by converting another feature map through cheap operations (denoted by wrenches). Using the SC, the ordinary convolutional layer accomplishes the transformation of the input feature \(X\) mapping to the output feature \(Y\):  
\[XeR^{c\times h\times w}\Rightarrow YeR^{h^{\prime}\times w^{\prime}\times n} \tag{1}\]  
where the convolution kernel in this process is \(fcR^{c\times k\times x\times n}\), the number of convolution kernels is \(n\), and the number of channels is \(c\). The number of FLOPs required for the convolution process is given as \(n\cdot h^{\prime}\cdot w^{\prime}\cdot c\cdot k\cdot k\), which is often up to hundreds of thousands since the number of filters \(n\) and the number of channels \(c\) are commonly very large. During the Ghost transformation, the convolution is used to generate \(m\) original feature maps: \(Y^{\prime}eR^{h^{\prime}\times w^{\prime}\times m}\)\((m\leq n)\). The \(s\) ghost feature maps are obtained subsequently by applying a series of cheap linear operations on each intrinsic feature in \(Y^{\prime}\). To maintain the same number of feature maps as the SC, the Ghost module obtains \(n\) feature maps as output data \((n=m\cdot s)\). The computation using the Ghost module is approximately to \(\frac{n}{S}\cdot h^{\prime}\cdot w^{\prime}\cdot c\cdot k\cdot k\) since \(s\ll c\). Therefore, the operating parameters are significantly reduced in Ghost convolution, i.e., the operating parameters in SC is \(s\) times of those in Ghost convolution. Figure 4: The structure of AEGG-FD. The Ghost bottleneck (G_bneck) structure is similar to the basic residual block which is composed of several convolutional layers and shortcuts. The first Ghost module achieves upscaling by increasing the number of channels, while the second Ghost module reduces the number of channels for matching the shortcut path. Two types of Ghost bottleneck residual modules are designed for two different convolution kernel step sizes, i.e., one and two, respectively. Their structures are shown in Figure 7, in which ReLU is used for the activation function, BN for batch normalization, and DWConv (Depthwise Separable Convolution) for deep convolution with stride = 2. In brief, when using the attention-enhanced ghost mode, the backbone of YOLOv5 will be reconstructed based on the Ghost bottleneck and the SE module. The GhostNet arrangement is essentially followed in the main body of the backbone, while the sequence and amount of Ghost bottlenecks are adjusted in the targeted manner. In contrast to  
Figure 5: Visualization of some feature maps generated by the first convolution in YOLOv5. Figure 6: An illustration of the convolutional layer and the proposed Ghost module for outputting the same number of feature maps. GhostNet, the Ghost bottleneck layers in the eighth and ninth layers are removed, and a cross-stage partial network with channel attention is added after the final standard convolutional layer. The first layer in the new backbone is a standard convolutional layer which uses 16 filters with kernel size 3 and step size 2, then 14 Ghost bottlenecks with increasing channel counts are stacked sequentially backward. Feature maps at different scales (from shallow to deep) are input individually into the feature pyramid structure at the fifth, ninth, and sixteenth layers.