## 2 Related Works  
### Highway Crack Detection  
The approaches for crack detection can be broadly classified into two main categories of traditional image processing and machine learning algorithms and deep learning based on detection networks. [PERSON] et al. [9] used a sample-based learning-in-sample paradigm to select a subset of the existing image database for unsupervised learning, classifying image blocks into two categories with or without crack pixels. [PERSON] et al. [10] proposed a Gabor filter-based method to detect transverse cracks and longitudinal cracks. The data for their experiment was captured for a car equipped with an imaging system, an inertial profiler, a differential GPS, and a webcam. [PERSON] et al. [11] proposed a crack detection scheme called CrackTree, in which geodesic shadows are first removed while preserving cracks, tensor voting is then used to build a crack probability map, and a set of crack seeds is finally drawn from the crack probability map to derive its minimum spanning tree, which is pruned at the edges of the tree to identify the desired cracks. [PERSON] et al. [12] proposed a hybrid crack detection and segmentation algorithm, the idea of which is to first obtain the rough location of the crack using histogram thresholding and then use mathematical morphology techniques and snake modeling techniques to further refine the location. [PERSON] et al. [13] proposed a method to find the minimum path from each path of length d through each crack pixel to detect cracks and proposed a corresponding dynamic programming implementation. [PERSON] et al. proposed a histogram of gradients (HoG) algorithm for crack detection, in which the idea is to perform local histogram computation by calculating the intensity and orientation of edges in each grayscale map. [PERSON] et al. [14] proposed a random structured forest land-based road crack detection algorithm. Although the traditional image processing algorithm works better on datasets with high crack recognition and simple road conditions, it is difficult to accurately and efficiently detect cracks in complex and diverse pavement environments with complex crack textures. With the development of deep learning, many deep learning-based crack detection networks have emerged. [PERSON] et al. [5] proposed an encoding-decoding network for crack detection, where a residual network is employed in the encoding part to extract features and migration learning is used to improve the network detection performance through data captured by a black box camera. [PERSON] et al. [15] proposed a method to detect road cracks based on a deep convolutional neural network and adaptive thresholding method, where the deep convolutional neural network is used to determine whether the image contains cracks or not, and a bilateral filter is then used for smoothing. [PERSON] et al. [16] proposed a two-stage convolutional neural network, where the first stage is used to denoise and isolate potential cracks to a region, and the second stage learns to detect the background of cracks in the region. [PERSON] et al. [17] proposed the use of Fast R-CNN for crack detection, and [PERSON] et al. [18] proposed a crack detection scheme using the scale invariant feature transformation (SIFT) algorithm to analyze the correlation between features to generate a series of graphs that are trained using a graph convolutional neural network and supervised using a super optimization algorithm. [PERSON] et al. [19] proposed an extended version of the U-Net framework, named MSK-UNet, for crack detection. They introduced selective kernel (SK) units to replace the standard convolution blocks in the U-shaped network to obtain receptive fields with different scales. Additionally, an image pyramid was established at the multi-scale input layer to preserve more image background information during the encoder stage. The datasets used in the above deep learning networks are mostly the data taken at close range.