# Multiclass Segmentation of Concrete Surface Damages Using U-Net and DeepLabV3+
## 1 Introduction  
Periodic and reliable inspections of structural properties in infrastructure systems are required to maintain their service capabilities and to ensure public safety. Engineers have constructed a methodology for conducting structural assessment, which is known as structural health monitoring (SHM). The stages in SHM are detecting the existence of damage, locating the damage, identifying the types of damage, and quantifying the severity of damage. Conventionally, the aforementioned stages rely on subjective human assessment, which greatly depends on the inspector's experiences. Due to the uncertainty, such procedures have traditionally been considered unsustainable due to frequent inaccurate damage assessment, which ultimately leads to inefficient budget and materials allocation. To tackle this issue, more quantitative and advanced statistical approaches have been proposed over the decades. Although there are many types of structural damages, automatic detection methods using computer vision techniques for concrete surface cracks were developed extensively. Methods such as thresholding, which segment the cracks based on the intensity of the color and continuous nature of the object, have been proposed [1; 2; 3]. Some related advancements, such as using edge detection and texture descriptor, were also proposedmboxciteB4-applsci-2192849,B5-applsci-2192849,B6-applsci-2192849. The results were fast and accurate for localized and controlled conditions. However, to make the model extensively deployable, it is crucial to consider real-world data scenarios. Such data often come with significant complexity in terms of foreground objects, which the traditional thresholding and edge detection method has difficulty dealing with. Moreover, it is also difficult to expand the model to deal with more types of damages [7]. Therefore, more-robust methods were developed using deep learning methods. One of the popular deep learning methods in computer vision technologies due to its accuracy and flexibility is the convolutional neural network (CNN). The CNN is an improvement of the traditional artificial neural networks by introducing sliding window techniques of a kernel that can efficiently extract features without using too many parameters. The main purpose of a CNN is to learn and classify features from images through supervised training. A CNN typically adopts a contracting shape that effectively reduces feature dimension and highlights important features. Earlier layers mostly highlight coarse features, such as texture detection, while deeper layers highlight class-specific features [8]. The CNN has been adapted to some object recognition tasks, namely image classification, object detection, and image segmentation. Image classification simply classifies the entire image, object detection predicts the location of an object on an image using bounding boxes, and image segmentation classifies every pixel in an image to its corresponding class. One advantage of image segmentation over object detection is that it allows for fine-grained, pixel-level detection of objects. In the damage detection task, it can be very useful for the area detection of a damage, which is useful for measuring the damage properties, such as the length and width of surface cracks. Advanced morphological feature extraction methods to measure crack dimensions have been implemented using data from segmentation output [9; 10]. A CNN built specifically for image segmentation outputs images with the same resolution as the input. In the typical CNN structure, the end layers consist of fully connected layers. A CNN for semantic segmentation replaces the fully connected layers with convolutional layers. As such, every layer is essentially convolutional. This method was introduced by [PERSON] et al. [11], resulting in an architecture called the fully convolutional network (FCN). This development was quickly adopted and modified to segment more complex objects.