# Road Crack Detection by Combining Dynamic Snake Convolution and Attention Mechanism
## 1 Introduction
[16] developed a deep convolutional neural network (DCNN) for crack detection based on a three-dimensional laser scanning dataset and determined the super parameters of the 36 DCNN system in accuracy and efficiency, but as the system relies on elevation change to detect the crack, the detection of shallow cracks is limited. [PERSON] et al. [17] proposed a road crack detection algorithm based on deep learning and adaptive image segmentation. The experimental results showed that the image classification accuracy of this method was around 98.7%, but some color images with a large number of noisy pixels cannot be segmented correctly. [PERSON] et al. [18] proposed a crack detection network based on the Atrous space pyramid module, which can reduce the computational complexity by acquiring multi-scale context information at a multi-sampling rate. Target positioning can be more precise, further improving the accuracy of detection. In the related research, [PERSON] et al. [19] established a model based on a U-Net model to quantify the severity of the disease and, finally, built a mixed model combining YOLO and U-Net to classify and quantify the severity of the disease, achieving a comprehensive assessment of road conditions. [PERSON] et al. [20] established a dataset of 3151 road surface images containing six disease types collected by UAV. This dataset was trained with three target detection models. The experimental results showed that the prediction performance of YOLOv3 was the best and most robust, but the detection accuracy of UAV still needed to be improved. [PERSON] et al. [21] addressed the issues of incomplete feature extraction in existing road pothole detection models and the practicality of detection equipment by proposing a lightweight object detection algorithm based on the YOLO BV-YOLOv5s. The model adopted a Bidirectional Feature Pyramid Network (BiFPN) structure for multi-scale feature fusion and utilized the Varifocal Loss function to optimize the sample imbalance problem. The experimental results demonstrated that the proposed model exhibited better performance and higher reliability in road defect detection. However, methods based on classification and object detection fail to reveal the presence and specific details of cracks at the pixel level. The advantage of semantic segmentation is its capability to classify objects at the pixel level, achieving precise segmentation and facilitating comprehensive semantic analysis. [PERSON] et al. [22] proposed a pavement crack identification method based on a deep convolutional neural network fusion model. The experimental results showed that this method can not only obtain pavement crack classification information but also provide accurate positioning and geometric parameters information. In order to identify the cracks in the image from other objects, [PERSON] et al. [23] proposed a two-step method of pavement crack detection, as well as segmentation, based on a convolutional neural network. It combined the improved YOLOv3 and U-Net with the segmentation method. The experimental results showed that this method had the advantage of high accuracy. [PERSON] et al. [24] constructed two network models, VGG Crack U-Net and Res Crack U-Net, to solve the semantic segmentation problem of crack images. The proposed model demonstrated significant advantages in pixel-by-pixel crack detection; however, it exhibited substantial issues with false negatives and false positive errors. [PERSON] et al. [25] proposed a model with deep convolutional neural network CurSeg as the core and integrated different scale features to obtain more context information. This model effectively suppressed noise propagation with strong robustness but showed limited effectiveness in detecting fine cracks and preserving crack integrity. [PERSON] et al.