# Road Crack Detection by Combining Dynamic Snake Convolution and Attention Mechanism
## 1 Introduction  
The continuous growth of highway traffic flow in China and the large-scale construction of roads in the past have entered the restoration period, and the concept of transportation infrastructure construction is constantly changing from \"building first\" to \"placing equal emphasis on construction and maintenance\". As one of the main sources of pavement diseases, if cracks are not detected and repaired in time, they will accelerate the deterioration of pavement, so the generation and severity of cracks are important indicators to be maintained [1]. At present, the road disease detection methods are roughly divided into three categories: image processing-based, machine learning-based, and deep learning-based [2]. The road disease detection method based on image processing includes a variety of technical paths, crack segmentation based on the image threshold [3], sealing crack classification [4] for two-dimensional pavement images based on threshold segmentation, and multi-scale wavelet transform algorithm to identify and segment the heterogeneous background pavement disease model [5]. However, the method based on threshold segmentation is usually difficult to select a correct threshold value for, and there are a largenumber of distractors on the road surface. The method based on image processing often has some phenomena, such as false detection and missed detection, with low detection accuracy and poor generalization ability. Machine learning (ML)-based methods include artificial neural network (ANN), Support Vector Machine (SVM), and Classification Regression Tree (CRT) [6]. [PERSON] et al. [7] combined smartphones and artificial neural network to realize automatic detection of road diseases, and the application method was simple and low cost. However, due to the low computational power and architecture efficiency, machine learning-based methods can only analyze some crack components [8]. [PERSON] et al. [9] elaborated on surface defect detection from the three aspects of traditional image processing, machine learning, and deep learning and analyzed that surface defect detection based on machine learning has low efficiency and generalization. To address the aforementioned issues, we proposed a semantic segmentation of road cracks based on an improved U-Net network. In this study, we use a U-Net [10]-based road crack detection model, and compared to large models, U-Net exhibits reduced overfitting in scenarios with limited data due to its smaller parameter size. Dynamic snake convolution (DSC) [11] and the Channel Spatial Attention Mechanism (CBAM) [12] were added to detect more complete cracks. In comparison to standard convolution, DSC can adapt to the geometries of different targets and can easily capture the characteristics of incomplete cracks. CBAM focuses on the characteristics of both channel and space, so it can learn the characteristics of small cracks. The experimental results showed that the proposed model can better detect the discontinuity between small cracks. With the rapid development of computer hardware and software, deep learning is considered an effective method for detecting road damage [13]. The deep learning method extracts the input data features layer by layer from the bottom to the top, learns from a large number of datasets to extract the relevant features, and automatically classifies and detects the input data through the learned features. At present, deep learning-based crack detection can be divided into three categories: image classification, target positioning, and semantic segmentation--classification to determine whether there are cracks in the image, target detection and positioning to produce a cover crack detection box, and semantic segmentation for the pixel-level prediction of the cracks in the image [14; 15]. In the study of image classification, [PERSON] et al.