## 2 Literature Review
### Crack Measurement Model  
The method of convolutional neural network is only aimed at the classification of images, that is, whether there is a crack, but it has no countermeasures for the measurement of the length and width of the crack. By only identifying whether there is a crack, without quantifying the crack, the information provided is insufficient. This is because in the infrastructure of concrete, asphalt, etc., the length and width of cracks are a key parameter in evaluating whether reinforcement is required. Based on the existing methods for automatic concrete crack detection using convolutional neural networks, [PERSON] and [PERSON] [54] proposed bounding box and image classification methods. This crack detection method is based on a deep FCN (fully convolutional network) for semantic segmentation of concrete crack images. Following validation by the concrete crack dataset consisting of 40,000 images of 227 \(\times\) 227 pixels, the average accuracy of the FCN network is about 90%. [PERSON] et al. [55] proposed a faster region-based neural network for detecting cracks on the concrete bridges with contaminated backgrounds, which can successfully detect tiny cracks from contaminated background after training. Although crack measurement based on deep learning has been a hot topic recently, there are still many unsolved problems, such as the large workload of crack measurement data annotation based on deep learning and the low accuracy of pixel segmentation. However, computer vision-based methods have the advantages of high efficiency and easy cost control. [PERSON] and [PERSON] [56] proposed a method to measure cracks with handheld devices, which combines statistical filtering (particle filtering) technology to automatically identify cracks in images and machine vision technology for measuring crack width and length. Image processing methods also have been extensively used in crack measurement. [PERSON] et al. [57] applied the Gabor filter method to texture defect detection. In this study, fifteen cracks were tested experimentally and it was found that the accuracy of the method for the length of the crack area reaches 95.19%, and the accuracy for the width of the crack area reaches 87.94%. In some dangerous areas and on higher floors, drone photography is more advantageous. [PERSON] et al. [58] proposed a UAV-based crack detection scheme. This paper proposes a GAN (generative adversarial network)-based fuzzy model to overcome the degradation of image quality caused by UAV motion. In addition, the idea of using local jump connections is introduced by identifying the strong correlation between blurring and sharpening. The proposed deblurring model is experimentally validated by studying the effect of jump connections on deblurring. The model is tested in comparison with the existing deblurring models, and the results show that the model significantly improves the deblurring performance in terms of both the overall structure and feature details of the cracked image. Based on the previous work, [PERSON] et al. [59] proposed an image processing technique, which can automatically measure crack features, including the width, length, orientation, crack pattern, etc. In the technique proposed by [PERSON] et al. [59], morphological techniques are used to rectify the inhomogeneous brightness of the background, and enhanced binarization and shape analysis are used to increase the detection performance. In addition, detailed algorithms for calculating crack length, width, and direction, as well as artificial neural networks to identify crack patterns, including vertical cracks, horizontal cracks, diagonal cracks, and random cracks, are also proposed. A series of experimental and analytical studies validate that the proposed technique can precisely measure and analyze the fracture characteristics. The literature review shows that PS-InSAR, convolutional neural networks, and digital image processing have achieved some interesting research results.