# Automated Multiple Concrete Damage Detection Using Instance Segmentation Deep Learning Model
## 1 Introduction  
Most of highly developed countries have difficulties in managing an increasing number of old civil structures. Currently, regular visual inspection is a widely used maintenance approach that considers the importance and scale of the structures. In South Korea, up to 270,000 structures require regular inspection annually, although the budget and number of inspectors are decreasing gradually. A 2017 report by American Society of Civil Engineers (ASCE) stated that 39% of all US bridges were over 50 years old, and the bridge maintenance costs were estimated at about $123 billion [1]. Thus, many industrialized countries are now seeking ways to inspect civil structures with reduced costs and labor without losing efficiency. For concrete structures, important types of concrete damage are designated to be inspected on a regular basis according to the aging mechanism of concrete. For most civil engineers, cracks, efflorescence, spalling, and rebar exposure are usually considered serious risks to the structural safety of concrete structures. A crack, which is a line on the concrete surface along with the concrete splits, is the most common type of concrete damage. A crack causes not only apparent damage on the concrete surface but also moisture penetration that leads to rebar corrosion. Efflorescence is a crystalline deposit, usually brightly colored, that forms on or near the surface of concrete structures because of concrete hydration. As a sign of concrete carbonation, efflorescence is used as one of the important indicators toevaluate the safety of the structure. Spalling is a phenomenon in which parts of the concrete surface detach, owing to rebar expansion caused by corrosion. The spalling of the concrete cover usually accompanies rebar exposure. Rebar exposure leads to corrosion by air moisture and can induce further corrosion of the rebar under the surface, which can reduce the loading capacity of the structure. Although the abovementioned concrete damage is recognizable by the naked eye, several shortcomings of visual inspection indicate a necessity of CV-based inspection to replace visual inspection. First, visual inspection is highly dependent on expertise as well as the condition of the inspectors. Second, visual inspection consumes a considerable amount of time and cost as an inspector carefully looks though a large area of concrete surface. Third, visual inspection is hazardous when assessing dangerous or restricted areas of structures. Breakdowns of aerial work platforms or catwalks are not rare events and can involve many casualties [2; 3]. To overcome the shortcomings of visual inspection, many research studies have sought to replace visual inspection with CV-based inspection using remote imaging devices such as drones and cameras. CV-based inspection can be more reliable and objective as it uses a designated computer algorithm. With a proper algorithm, the inspection can be automated by a computer, and the time and cost of inspection can be significantly reduced. Furthermore, remote imaging reduces the risks to inspectors in assessing dangerous areas. Given these advantages over conventional visual inspection, many studies have reported on CV-based inspection, and most of them focused on concrete crack detection. A variety of studies reported on crack detection methods ranging from conventional approaches using algorithm-based image processing [4; 5; 6; 7; 8; 9; 10] to recent attempts that adopted deep learning techniques [11; 12; 13; 14; 15]. [PERSON] et al. [4] conducted a comparison of various image processing algorithm such as fast Haar transform for crack detection algorithm. [PERSON] et al. [5] presented crack width method with an application of two different camera techniques. [PERSON] et al. [6] proposed a crack detection method considering direction of crack and tested its performance on images captured from real structures. [PERSON] et al. [7] used terrestrial laser scanning method to measure cracks in 3D space. [PERSON] et al.