# Improvement of Damage Segmentation Based on Pixel-Level Data Balance Using VGG-Unet
## 2 Literature Review  
The application of multiple layer structured Convolutional Neural Networks (CNN), such as VGG16 [16], AlexNet [17], ResNet [18], GoogLeNet [19], DenseNet-121 [20], make it possible to develop approaches with higher accuracy to classify the damages from the images taken by cameras installed on UAVs or robots. For example, Deep Learning-based image classification have been conducted to detect cracks on the concrete [21], cracks on the road [22], or defects on the steel [23]. It can find the images with damages and tell you which categories the damages belong to. However, these traditional classification methods using CNN for damage classification are unable to determine the location of the damages from the images, since only the categories of the damages are known by the CNN models. To locate the damages from full images, Faster Region-based Convolutional Neural Network (Faster R-CNN) [24] is studied to make bounding box for detected objects, named as object detection. In inspection of structural damage in engineering, object detection is adopted to detect and locate cracks on road [25], cracks on concrete [26], bricks on historic masonry structures [27; 28], or even multiple damages [29]. Another network for object detection technique is YOLO [30], and it also be taken successfully for damage detection on bridges [31]. However, object detection techniques are still at the grid-cell level for detecting multiple damages. It means that characteristics of the included damage, such as size, cannot be inspected directly, even though objective images must be cropped into little patches. Image segmentation using Fully Convolutional Network (FCN) is an excellent mean to detect and predict objects in the images, pixel to pixel, to show its location and size [32]. FCN is one of extended CNN because it converts predicted results from a class of full image to a class of each pixel and finally shows a semantic segmentation image, by adopting upsampling layers to upsample convolutional layers. It is successfully used in detecting structures from aerospace images of several cities [33]. For damage detection, [PERSON] et al. proposed FCN-based cracks prediction method for images of concrete [34] and [PERSON] et al. have proved the feasibility of crack width calculation from segmentation results [35]. The more powerful feature of FCN is that it can be taken to detect and label the structural components from images. For example, rubber bearing detection [36] or even whole bridge component recognition [37]. Segmentation method can detect structural damage robustly from real world images in more universal sources. Most of segmentation for damages were proposed for laboratory photographs with fix camera to object distance and angle to recognize the damage correctly. However, some components are installed under the deck of bridge, where the lighting condition is not so ideal for detecting damages. And because of influences by safety and environmental factors, high-resolution images provided by cameras installed on UAVs or robots could not always be in a fix distance or angle. Therefore, the proportion of damaged pixels is often small in the data set composed of such images. Due to these factors, the capability of FCN models for minor damage detection is sometimes severely affected. In addition, the traditional segmentation method, Squashing Segmentation, uses the data set composed of squashed full images with small size [17]. In order to adapt these networks, the images from the data set are usually squashed before training and testing, even though the modern cameras provide high-resolution images. The process of compression would lose part of object's feature information from the original images, leading to the decrease of accuracy. Hence, the traditional segmentation method is not much suitable for damage detection in high-resolution images.