## 2 Methodology
### Network Models
#### 2.2.2 Model Selection  
Due to variations in structure and number of layers, multiple network models can be derived from the same network architecture. These models differ in their ability to extract image features to varying degrees. [PERSON] et al. conducted an extensive analysis of multiple state-of-the-art deep neural networks on important metrics such as accuracy, memory usage, and parameters in the ImageNet image categorization challenge task, which not only showed the excellent performance of these networks in the classification task but also provided some insights for other researchers in the selection and design of neural networks [22]. In 2012, AlexNet achieved a Top-1 accuracy of 73.8% in the ImageNet image classification challenge, marking a milestone in deep learning and demonstrating the power of deep CNNs [23]. The VGG architecture, renowned for its deep network structure at the time of its proposal, focuses on feature extraction by increasing the number of convolutional layers [24]. This architecture showed that adding more convolutional layers can indeed improve feature extraction results. The Inception architecture is a fundamental element of GoogLeNet, balancing model efficiency and accuracy by introducing techniques such as multiscale convolution and sparse connectivity [25]. Over time, several versions of the \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Distress Category** & **Train Dataset** & **Val Dataset** & **Test Dataset** & **All Dataset** \\ \hline loose\_1 & 5275 & 879 & 2639 & 8793 \\ loose\_s & 418 & 69 & 211 & 698 \\ poor\_1 & 7176 & 1196 & 3588 & 11,960 \\ water\_1 & 6113 & 1018 & 3058 & 10,189 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Number of distress samples. The total number of samples for each type of distress is included, as well as the number of samples for each type of distress in the training, validation, and test sets. Inception architecture have been developed, including InceptionV2 and InceptionV3. However, for large and deep complex models such as VGG and GoogLeNet, as the number of layers continues to increase, the gradient gradually decreases and converges to zero during the training process, resulting in inefficient weight updates. [PERSON] et al. proposed ResNet in 2015, which introduced jump connections to solve the gradient vanishing problem, and this important residual structure enabled proper training of deeper networks [26]. In 2016, DenseNet was proposed, which introduced dense connections to facilitate full feature transfer and sharing [27]. Compared to ResNet, DenseNet has better feature transfer and fewer parameters. However, in scenarios with limited computational resources, large models may be too bulky. Therefore, lightweight networks such as MobileNet [28], SqueezeNet [29], and EfficientNet [30] have been proposed. These networks maintain high accuracy rates while reducing parameters and computation through structural design and model compression. In the field of highway distress identification and detection, the above network architecture has been widely used. In the classification task, [PERSON] et al. adopted DenseNet and deconvolution network framework for pixel-level detection and combined it with a support vector machine classifier to classify cracks, achieving 98% accuracy [31]. [PERSON] et al. constructed EfficientNet and MobileNet models, respectively, to identify pavement water seepage problems, and the research results showed that EfficientNet performed well and could accurately detect pavement water seepage [32]. [PERSON] et al. used generalized Morse wavelet (GMWs) to perform continuous wavelet transform on vehicle vertical acceleration data for the detection of transverse cracks in the road surface and fine-tuned GoogLeNet, SqueezeNet, and ResNet18 [33]. Experimental results show that SqueezeNet has higher sensitivity in the validation phase. [PERSON] et al.