## 3 Method
### Structure  
#### 3.2.1 Backbone  
As shown in Figure 3, the backbone mainly includes five feature extraction modules and an SPPF (Spatial Pyramid Pooling--Fast) module. The first feature extraction module, CBS, is composed of Conv, Batch Normalization, and SiLU activation function. The remaining four feature extraction modules are composed of GCBS and LF modules designed by us. Unlike CBS, GCBS consists of GhostConv, Batch Normalization, and SiLU activation function. The difference between the four feature extraction modules is that block1, block2, block3, and block4 contain 1, 2, 3, and 1 LF modules, respectively, which can achieve different levels of feature extraction for feature maps of different sizes. In the backbone, we use GhostConv to achieve the goal of model lightweight. Compared with ordinary convolution, some feature maps in GhostConv have depthwise separable convolution operations, which reduces the number of parameters used. However, at the same time, it also leads to the problems of incomplete extracted hole feature information  
Figure 3: Backbone Structure. The left part is the structure of the backbone, and the right part is the size and number of channels of the output feature map of each module. and low detection accuracy. Therefore, we designed the LF module to enhance the feature extraction capability of the feature extraction network. As shown in Figure 4, in the LF module, we first use two CBS feature extraction modules to halve the number of channels in the original feature map. Then, in order to suppress the unimportant information in the feature map, pay attention to the effective information of the pothole, and improve the detection accuracy, we integrate the ECA (Efficient Channel Attention) attention module [48]. First, the global average pooling operation is performed on the feature map, and the channel compression is performed to obtain the average value of the entire feature map. Then, a 1 x 1 convolution operation is carried out for feature extraction and sigmoid activation function operation to obtain the weight information of the feature map. Finally, it is multiplied with the original feature map to obtain a new feature map with greater attention to potholes. To make the LF module lightweight, we use depthwise separable convolution to extract feature information. In order to compensate for the feature loss caused by the depthwise separable convolution, we conduct an attention operation again to focus on useful information. Here, we will splice the output feature map and the original feature map on the channel to fuse the feature information. The number of channels is equal to the number of channels before the LF module is input. Then, we perform a 1 x 1 convolution operation to extract the feature information. Finally, we use the attention module again to filter out unimportant information. To sum up, our LF module has fewer parameters and can pay more attention to important information. The LF module Formulas (1)-(5) we designed are as follows:  
\[CBS=\text{SiLU}(BN(\text{Conv}(x))) \tag{1}\]  
\[ECA=x\cdot(\text{Sig}(\text{Conv}(\text{Avg}(x)))) \tag{2}\]  
\[P_{1}=CBS(ECA(CBS(x))) \tag{3}\]  
\[P_{2}=(DW\,\text{Conv}(P_{1}))\,\text{Cat}(P_{1}) \tag{4}\]  
\[y=\text{ECA}(\text{CBS}((ECA(P_{2}))\,\text{Cat}(\text{CBS}(x)))) \tag{5}\]  
where \(x\) is the input feature map, \(Conv\) is a convolution operation, \(BN\) is Batch Normalization, \(SiLU\) is SiLU activate function, \(CBS\) is the CBS module, \(Avg\) indicates the global average pooling operation, \(Sig\) is sigmoid activate function, \(\cdot\) is point multiplication operation, \(ECA\) is an ECA module, \(P_{1}\) is the output feature map before lightweight and \(P_{2}\) is the output feature map after lightweight, \(DWConv\) is a depthwise separable convolution, \(Cat\) is concat operation, \(y\) is the output feature map.