# Automated Multiple Concrete Damage Detection Using Instance Segmentation Deep Learning Model
## 1 Introduction
[8] demonstrated a new automated crack identification method using 3D model constructed by structural from motion techniques. [PERSON] et al. [9] proposed a novel crack detection algorithm, spatially tuned robust multi-feature classifier and tested the algorithm with images captured by a pavement scanning robot. [PERSON] et al. [10] compared various crack detection method and conducted parametric study for each image processing method. In concrete crack assessment, it is also important to measure length and width of concrete cracks to evaluate structural integrity of concrete structures. Besides the developments of concrete crack detection methods using image processing, many researchers reported computer vision-based crack quantification methods. [PERSON] et al. [16] proposed crack assessment procedure using 3D point cloud and measured crack width using image skeletonization techniques. [PERSON] and [PERSON] [17] proposed crack quantification methods that refines crack area detected by a deep learning model and measure the width using sequential image processing. [PERSON] et al. [18] combined conventional Otsu thresholding and deep learninig technique for crack width measurement. As mentioned above, it is important not only to evaluate cracks, but also to detect and quantify damages such as efflorescence, spalling, and rebar exposure for the integrity evaluation of concrete structures. Research that tried to detect concrete damages used various types of equipment and presented methodologies that detects the appearance or chemical properties of the damage.As efflorescence is recognizable by the naked eye, the number of studies on developing an efflorescence detection technique is limited. However, research studies on detecting efflorescence using special devices such as a hyperspectral camera or a laser scanner (i.e., LiDAR) have often been reported [19; 20; 21; 22]. Damage types such as spalling or potholes are detected based on the dent features that appear when they are captured in images [23; 24; 25; 26; 27]. Rebar exposure is often accompanied by cracks and/or spalling. Therefore, there have been studies that detected rebar exposure with other concrete damage, rather than single-class detection of rebar exposure [28]. Despite many studies, there have been few examples that apply CV-based inspection techniques to real structures that have diverse types of objects in the vicinity such as accessories (e.g., electrical lines and drainage pipes) or surrounding objects (e.g., nearby passengers and vegetation). In particular, the separation of similar objects from the damage may be poorly performed, although the features are carefully crafted to distinguish damage from the objects [6; 9; 29; 30]. To overcome the limitations of human-crafted feature-based inspection methods, several damage detection methods have used deep learning techniques, which train a computer to automatically define features of damage from a large number of images [11; 12; 13; 14; 15]. [PERSON] et al. [11] detected multiple types of damage including concrete cracks, steel corrosion, bolt corrosion, and steel delamination using Faster R-CNN (Region-based Convolutional Neural Network) [31], and the result displayed the damage using rectangular bounding boxes without illustrating the damage shapes. [PERSON] et al. [12] also used Faster R-CNN [31] to detect multiple types of concrete damage in masonry historic structures. [PERSON] et al. [13] examined the possibility of unmanned-aerial-vehicle-based road damage inspection and discussed the current limitations owing to the lack of a reliable dataset. [PERSON] and [PERSON] [14] successfully detected concrete damage in the form of bounding boxes in a tunnel lining in dark illumination conditions. [PERSON] et al. [15] implemented a fully convolutional network (FCN) [32] to detect multiple concrete damage such as cracks, spalling, efflorescence, and holes.