## 1 Introduction
It is important to note that, compared to classification [23; 24; 25] and detection [26; 27; 28], pixel-level segmentation [5; 6; 29] can provide more accurate geometric target descriptions for a wide range of applications, such as geometric feature quantification of distress, severity classification, and quantitative assessment of pavement condition, which is the focus of this article. For example, [PERSON] et al. [30] proposed a semantic segmentation algorithm for road cracks based on U-Net [31], but the model generalization was insufficient due to the lack of data (80 training images and 20 validation images). Subsequently, researchers have made a series of improvements based on U-Net [32; 33] and achieved improved segmentation performances in crack segmentation test sets such as CRACK500 [34], CFD [35], and A  
Figure 1: The challenge of the automatic detection of multi-type pavement distress [20]. address the problem of the small size of the existing crack segmentation datasets, a crack segmentation network known as ConnCrack, which integrates a generative adversarial network (GAN) and connectivity graphs, was proposed by [PERSON] and [PERSON] [37]. However, pavements do not have only one type of distress, and each type of distress has its own exclusive characteristics and apparent forms. For this reason, [PERSON] et al. [38] applied a U-Net-like network architecture with different levels of contextual resolution to integrate more contextual information. The authors considered different types of pavement distress, but they only extracted the planar regions of the different types of distress. In addition, [PERSON] et al. [8] collected pavement images of urban roads in Montreal in Canada and produced a semantic segmentation dataset for potholes, patches, lanes, linear cracks, and mesh cracks. They also proposed and evaluated a method to automatically detect and classify the types of pavement distress using a convolutional neural network (CNN) and low-cost video data, where the model's detection and classification accuracies both reached 83.8%. It is important to note that, affected by the difficulty of dataset collection and the cost of labeling, until recently, there have been very few studies on the semantic segmentation of multiple types of pavement lesions in complex scenarios. Although the above DCNN-based algorithms have achieved good distress segmentation performances, CNNs still do not perform well in extracting long-range contextual information, which is crucial for the model to understand distress with complex topological features in pavement images [39]. In addition, since the convolutional kernel size of the CNN (3 \(\times\) 3) determines that the model can only understand input imagery with localized information, the small receptive field leads to discontinuous and false-positive predictions [40]. Fortunately, since the first successful application of the vision transformer (ViT) [41] in computer vision tasks, there has been an explosion of dense prediction research based on ViT models, which tokenize the input image and then utilize a self-attention mechanism to enable the model to comprehend the image with a global perspective [42; 43]. However, these ViT-based improvements tend to significantly increase the computational cost. In addition, since the resolution of the positional encoding in the ViT model is fixed, it causes a degradation in the performance of dense prediction when the resolution of the test image is different from the resolution of the training image. For this reason, [PERSON] et al. [44] proposed a hierarchical transformer encoder, replacing the positional encoding with a 3 \(\times\) 3 convolution operation, which greatly improves the robustness of the model segmentation. However, the use of only a few simple multilayer perceptrons (MLPs) for decoding is likely to have reduced the model's ability to restore detailed information. Quantitative assessment of pavement technical conditions is a prerequisite for the rational development of maintenance programs.