# InstaDam: Open-Source Platform for Rapid Semantic Segmentation of Structural Damage
## 1 Introduction  
Visual inspections to assess the condition of civil infrastructure are time-consuming, repetitive, and put inspectors at high levels of risk. The automation of visual inspection taskscan greatly enhance the overall efficacy and efficiency of condition assessment by allowing inspectors to focus on higher level decisions rather than repetitive tasks. Supervised deep learning methods have proven successful in a key step towards this goal of automation, namely the automated identification of multiple types of damage [1; 2; 3; 4]. To realize a fully automated system for inspections, however, higher fidelity information needs to be extracted from images, including, for example, the material type, as well as other damage-like features such as cables, shadows, etc. Different approaches have been examined for these inspection tasks, including (i) object detection, where a bounding-box is drawn around the damaged region [5; 6; 7; 8; 9], and (ii) semantic segmentation [1; 10; 11; 12; 13], where each pixel is classified as a certain damage type. While these methods have shown tremendous success, researchers often develop their own private datasets for each new study, making rigorous benchmarking and comparisons of advances in deep learning architectures difficult. For other applications of semantic segmentation, such as self-driving, the availability of large and open datasets such as Cityscapes [14], SYNTHIA [15], KiTTy [16], BDD100K [17], etc. for scene understanding has helped advance research into deep learning methods to a point where they are now being directly applied in the self-driving industry at scale. The availability of similar large open datasets for semantic segmentation of damage in civil infrastructure will facilitate advances towards automation of civil infrastructure inspection. To annotate datasets for image segmentation, researchers [18; 19; 20] often use software such as LabelMe [21], Matlab [22], and Photoshop [23]. More recently, many commercially available and web-based annotation packages for image segmentation have sprung up, such as Labelbox [24], Prodigy [25], Hasty [26], and Kili Technologies [27], among others. These solutions offer a variety of general labeling tools such as dots, lines, polygons, bounding boxes, pen (lasso), and superpixels. Additionally, the available applications support different degrees of user and project management, ranging from locally hosted applications with no notion of user or project management to full-fledged web-based applications that support full team collaboration. Most of these general-purpose annotation software packages have been designed for use in applications such as scene segmentation, where buildings, roads, pedestrians, signs, etc. are the main items of interest. For the annotation of amorphous shapes like cracks or spalling, the methods available in state-of-the-art software require too many clicks from the user, all of which must be extremely precise, making the annotation process very difficult, prone to inaccuracies, and time consuming. A more efficient software platform is thus needed that will enable rapid development of large and complex annotated datasets for damage. In this paper, we present an open source software platform, termed InstaDam, for rapid annotation of damage for automation of visual inspections. By incorporating binary masks to aid user input for annotation of different damage types, InstaDam helps to increase the efficiency and consistency of annotations. These binary masks are generated by applying image processing techniques (IPT) to the image being annotated. Several IPTs that researchers have identified for automated detection of damage are implemented for use in InstaDam, allowing for annotation of a wide variety of damage types. The parameters of IPTs can be quickly modified to fine tune the mask edges. InstaDam includes a suite of standard annotation tools found in most annotation software.