# SE-Lightweight YOLO: Higher Accuracy in YOLO Detection for Vehicle Inspection
## 4 Approach to the New Change of the Improved YOLO Network Model
### Squeeze-and-Excitation Attention  
SE attention mechanism can be divided into two steps: squeeze and excitation. The brief algorithm architecture is in Figure 5 below.  
Standard convolution by itself makes it challenging to model channel relationships. It extracts data-related features by sliding convolution kernels on the input data, thus making it challenging to capture long-range channel features and global features by obtaining only local features. Explicitly establishing channel interdependencies can increase the model's sensitivity to information channels and improve model robustness. In addition, using a  
Figure 4: Architecture of SPPFCSPC module.  
Figure 5: Algorithm of SE block.  
Figure 3: Architecture of SPPCSPC module.  
global average pool can help the model capture global information by averaging the values of each channel across the feature map to obtain further global information, which helps the model obtain aggregated information about the entire input, which is precisely what convolution lacks. In the following, given the input I, the squeezing step for the kth channel is shown in the following equation:  
\[z_{c}=\frac{1}{H\times W}\sum_{i=1}^{H}\sum_{j=1}^{W}x_{c}(i,j) \tag{1}\]  
We denoted the input as \(X\). \(c\) is the \(c\)-th iteration. Next, excitation, targeted at fully capturing channel dependency, can be expressed as  
\[\hat{X}=X\cdot Sigmoid(\hat{z}) \tag{2}\]  
where \(\cdot\) refers to channel-wise multiplication, and \(\hat{z}\) is the outcome calculated using the transformation function displayed below. Sigmoid refers to the Sigmoid function. Furthermore, \(\hat{z}\) is calculated using the formula below:  
\[\hat{z}=T_{2}(ReLU(T_{1}(z))) \tag{3}\]  
In Equation (3), \(T_{1}\) and \(T_{2}\) are two linear transformations that can be learned to capture the importance of each channel.  
As a critical element for reaching state-of-the-art performance, the SE block has been widely used in recent mobile networks. However, it only considers reweighing the importance of each channel by modeling channel relationships but neglects positional information.  
Feature maps capture different features at different locations in the input image and reflect the model's understanding of the input data. Feature maps play an essential role in convolutional neural networks; their generation and delivery help the network understand and extract features from the input data, thus enabling the effective learning of complex tasks. We can discover more important feature information through feature maps, which is convenient for us to enhance later. The specific location where the primary feature information is concentrated can be seen in Figure 6. By comparing Figures 6 and 7 below, it is clear that the essential features associated with the vehicle have been significantly enhanced. Take the third picture in the first row of Figure 7 as an example: the outline of the vehicle in this picture is significantly enhanced compared to the corresponding position photo in Figure 6.  
Figure 6: Initial feature map display.