## 1 Introduction  
Pavement distress detection and damage condition evaluation have been hot issues in road maintenance decision-making [1; 2]. Generally, road maintenance departments use mobile acquisition equipment to obtain pavement images, conduct manual visual inspection, and finally, obtain the pavement condition index (PCI) of the line evaluation unit [3]. This process has a long detection period, high cost, and strong subjectivity, which makes it difficult to meet the demand for the rapid and large-scale automatic detection and evaluation of pavement technical conditions [4]. The primary goal of this study is to design a pavement distress segmentation model for multiple types of pavement distress in complex scenarios based on the latest deep-learning techniques and quantify the pavement distress characteristics for the automated evaluation of pavement conditions. As pavement cracks, potholes, and repairs in images are usually shown as linear and planar structures with shape variations, pavement damage detection can be regarded as a linear and planar detection task in computer vision [5; 6; 7; 8; 9]. Most of the early studies focused on crack detection algorithms based on digital image-processing techniquesand machine learning. The former are mainly represented by methods such as threshold segmentation [10], edge detection [11], morphological operation [12], minimal path selection [13], and region growth [14]. The latter, which can be predicted by learning the intrinsic knowledge of pavement crack data, are mainly represented by support vector machine (SVM) [15; 16; 17] and shallow artificial neural network (ANN) [18] models. Ideally (Figure 1a), if a single scene is captured and the distress has good continuity and high contrast, then the early methods based on low-level features can achieve a high accuracy in pavement distress detection [5]. However, during the acquisition of images of pavements, which is affected by factors such as the pavement type, lighting conditions, interferences, and stains, the above detection methods, despite being able to quickly obtain part of the information on the pavement lesions, cannot easily take into account the influence of multi-source noise with textural similarity, and the completeness, accuracy, and efficiency of the identification are not good. It is worth noting that most of the application scenarios are complex ones. Therefore, before carrying out the research described in this article, the \"complex scene\" (Figure 1b) in the scope of the subsequent experiments and discussions was defined as follows: (1) the scene contains different pavement types, multiple types of pavement distress, and a complex topology; (2) the scene has a pavement background with strong speckle noise that is complex and changeable, with a low target signal-to-noise ratio and poor spatial continuity of the target pixels; and (3) there may be shadows, occlusions, varied light intensity, and other factors that influence the recognition of pavement. These factors change, and there can be noise with textural similarity to pavement lesions [19]. Therefore, automatic pavement distress detection from CCD images of pavements acquired in real projects is still a challenging task. Recent theoretical research advances have shown that deep learning can autonomously acquire different levels of features and then construct high-level features from the low-level primitives to effectively solve complex fitting problems [21]. Influenced by this, deep-learning-based methods now play a dominant role in pavement damage detection [19; 22]. Research on pavement damage detection based on deep convolutional neural networks (DCNNs) can be categorized into three types: image classification methods, target detection methods, and semantic segmentation methods.