# A Road Crack Detection Method Based on Residual and Attention Mechanism
## 1 Introduction  
The traditional road crack detection method records the location and degree of damage of cracks by investigators driving along the road. This method has the disadvantages of low efficiency, high human resource consumption, and significant subjective factors, and can no longer meet the increasing demand for road maintenance [1; 2]. Therefore, it is very necessary to achieve automated detection of road cracks. Early machine vision-based image processing algorithms were sensitive to interference factors such as noise, lighting changes, and complex backgrounds in images, which could easily lead to inaccurate processing results, such as median filtering [3], threshold segmentation [4], edge detection [5; 6], etc. In recent years, with the development of artificial intelligence [7; 8], deep learning methods have been applied to road crack detection tasks.  
The early deep learning-based pavement crack detection methods mainly adopted the classic fully convolutional network (FCN) and convolutional neural network (CNN) architectures. FCN is an image semantic segmentation model derived from CNN, which achieves pixel-level semantic segmentation by replacing traditional fully connected layers with fully convolutional layers. However, due to the pooling and upsampling operations of FCN, the network may experience a certain degree of spatial information loss during processing. In order to reduce information loss, [PERSON] et al. created a U-Net network model based on CNN architecture and applied it to biomedical segmentation [9]. U-Net adopts a skip connection structure to retain global image information and extract better features. U-Net and its encoding and decoding structure are widely used, and [PERSON] et al. [10] first used the U-Net network model in crack detection, achieving higher accuracy than FCN. [PERSON] et al. [11] proposed a crack detection method based on the Faster RCNN model and combined it with an improved feature extraction network, which has good crack detection accuracy. [PERSON] et al. [12] proposed a network consisting of extended convolution, spatial pyramid pooling, and skip connection modules. [PERSON] et al. [13] improved the accuracy ofdetection by designing multi-layer extension modules and hierarchical feature learning modules. The above methods utilize the idea of multi-scale feature fusion to effectively improve the performance of crack detection, but are susceptible to noise and have the problem of missed detections. [PERSON] et al. [14] used residual networks to extract crack features. [PERSON] [15] proposed a convolutional neural network using severely imbalanced data for crack detection on road surfaces. [PERSON] et al. [16] proposed a road crack detection network called DeepCrack based on SegNet, which combines the same scale features output by the encoding and decoding layers in pairs, and then concatenates and fuses all the fused images as the output of the network. [PERSON] et al. [17] proposed the CrackNet crack detection network, which was designed without the use of pooling layers, resulting in detection errors. Although the above methods have improved segmentation accuracy compared to manual methods, there are still some shortcomings. Current crack detection methods are difficult to ensure accurate detection of small cracks, and the detection results are easily affected by noise, resulting in missed detections.  
Taking inspiration from the above literature, this paper proposes a road crack detection model based on residual and attention modules in an encoder decoder structure to address the problem of traditional road segmentation algorithms being difficult to identify narrow cracks and inaccurate segmentation edges. The rest of this article is organized as follows. Section 2 briefly introduces the relevant work. Section 3 introduces the details of the experimental Implementation and the corresponding experimental results. Section 4 is the conclusion.