# Analytical Method for Bridge Damage Using Deep Learning-Based Image Analysis Technology
## 3 Damage Detection Methods  
### Review of Deep Learning-Based Analytics  
We studied the ResNet and U-Net algorithms, which are two of the most commonly used artificial neural network techniques for deep learning image analysis. Each algorithm can be analyzed in two ways. The LH-RCNN technique detects damage in the image in the form of a box based on the damage data trained by the detection method. For example, in the case of a crack, the range covered by the crack is detected as a box region, and further analysis is required to analyze the exact damage area. [PERSON] et al. (2019) [21] proposed a novel two-stage detection method for faster and more accurate analysis by combining the RoI-based analysis techniques of Faster-RCNN and R-FCN. A study was conducted to significantly improve the analysis speed based on ResNet while maintaining the analysis performance of the existing RCNN. [PERSON] et al. (2016) [22] found that converting the images to grayscale increases the CNN performance during object recognition. Similarly, [PERSON] and [PERSON] (2018) [23] found that grayscale images improve the CNN performance in lung disease classification. For crack detection, [PERSON] et al. (2020) [24] proposed improving images to determine the image quality and preprocessing data to improve the results. The U-Net method is an end-to-end fully convolutional network-based model proposed for image segmentation in the medical field. Image work in the medical field previously relied only on expert analysis, but with the advent of deep learning models, the analysis of X-rays, MRI images, and so on in the medical field is increasing with the help of deep learning. In medical research, automated microscopy experiments generate a large \begin{table}
\begin{tabular}{c c c} \hline \hline
**Class** &
\begin{tabular}{c} **Before Augmentation** \\ **(Original Images)** \\ \end{tabular} & **After Augmentation** \\ \hline Crack & 22,863 & 91,452 \\ Scaling/spalling & 1587 & 6348 \\ Reinforcement exposure & 5364 & 21,456 \\ Leak & 1962 & 7848 \\ Corrosion & 2148 & 8592 \\ \hline Total & 33,924 & 135,696 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Collected data. amount of image data, and for each image, cells and cell components are classified using semantic segmentation methods. Therefore, they achieve excellent performance on several biomedical image segmentation problems using data augmentation with only a small amount of training data by utilizing the U-Net structure. [PERSON] et al. (2015) [PERSON] et al. (2015) [PERSON] et al. (2015) applied contemporary classification networks (AlexNet, VGGnet, and GoogLeNet) to fully convolutional networks and studied techniques to obtain fine-grained trained results through segmentation tasks. [PERSON] et al. (2015) [PERSON] et al. (2015) termed the U-Net technique U-Net because the shape of the algorithm configuration resembles a \"U\" and has the configuration shown in Figure 3. The technique consists of a symmetrical network for obtaining the overall contextual information of the image and a network for accurate localization. The network on the left is configured to detect the context of the input image with a contracting path and has a VGG-based architecture similar to FCNs. The network on the right is the expanding path, which is configured for detailed localization and combines high-dimensional up-sampling with thin-layer feature maps. When training the actual network, because U-Net is a network for segmentation, we used Softmax on a pixel-wise basis. Softmax is an activation function used in multiclass classification in which three or more classes are classified, and it has the property of the input values all being normalized to values between 0 and 1 as the output. Moreover, the sum of the output values is always 1. Given n classes to be classified, an n-dimensional vector is input to estimate the probability of belonging to each class. Equation (1) is a Softmax that finds the probability value for every pixel, allowing the probability value for each pixel to be predicted.