# Deep Learning-Based Automated Background Removal for Structural Exterior Image Stitching
## 1 Introduction  
Monitoring the integrity of aging structures has become increasingly important in terms of extending structures' service life and saving maintenance costs. For effective monitoring of large-scale structures, unmanned aerial vehicles (UAVs) have recently played a key role, in that faster and safer inspection is possible than expert-dependent visual inspection, even for inaccessible areas by human beings [1, 2, 3]. One of the most popular UAV-based inspection strategies is that structural exterior damage can be effectively assessed by using UAV-captured digital images. However, damage assessment and making decisions from a number of digital images often be labor-intensive and unreliable, especially as the target structure gets larger. In particular, damage quantification as well as localization are challenging works without structural exterior map establishment. To tackle the technical issues, digital image stitching techniques have been widely accepted for entire structural exterior mapping [4, 5, 6, 7]. As for precise structural damage quantification and localization including micro-scale damage, the close-up and high-resolution spatial scanning of a digital camera-mounted UAV along the entire structural region of interest (ROI) is often required [8, 9, 10, 11]. To construct structural exterior maps using the digital images scanned along a large-scale structure, optimal digital images should be selected by considering the overlap ratio between adjacent digital images to be stitched. The use of all raw digital images for structural exterior map establishment is not effective in terms of high computational cost as well as image stitching accuracy. To address the optimal digital image selection issue, several techniques have been investigated. [PERSON] et al. [12]used a constant time interval technique, which extracts video frames every two seconds. This technique can reduce the spatial redundancy of the acquired video frames, but it cannot meet the constant overlap ratio between the selected digital images. Then, to ensure the constant overlap ratio, [PERSON] et al. [13] proposed a key frame selection technique with the known operational condition of UAV. Similarly, [PERSON] et al. [14] employed monocular simultaneous localization and mapping (SLAM) to stitch UAV-scanned images in real-time. They calculated the relative distance among adjacent images through the weighted combination of translation and rotation in large-scale direct SLAM, and the key frames were then selected by using a certain threshold. However, in the UAV's close-up vision scanning, digital images often include the target ROI and unnecessary background together especially in the edge of a target structure. The background objects such as sky, mountain, river, tree, etc. disturb stitching as well as selecting optimal digital images, because the background objects have extremely different feature variations from the target structural ROI ones on the sequentially scanned images. In addition, there are more distinguishable image features in the background objects than the repetitive and local target ROI ones, which pose serious distortion and ghosting effects on the image stitching process. To solve this problem, a number of trials have been conducted. For example, [PERSON] et al. [15] proposed a self-adaptive optical flow technique to detect target object regions on the sequential image data. They tried to enhance the object outlines from a rough optical flow field using local mean algorithm, and the target object regions were then extracted. In addition, [PERSON] and [PERSON] [16] studied a multiple moving object tracking technique, enabling them to achieve robustness against objects' occlusion, shadows and camera jitter by combining background subtraction and k-means clustering. More recently, [PERSON] et al. [17] proposed a deep learning network, called Tiramisu trained with common objects in context (COCO) dataset, which segments target objects for background removal.