# Deep Learning-Based Automated Background Removal for Structural Exterior Image Stitching
## 3 Experimental Validation  
The proposed technique was experimentally validated using a vision camera mounted-UAV at an in-situ 18 story building. The overall test procedures were as follows. First, the vision camera mounted-UAV scanned the target structure along a predefined scanning path to acquire the spatially continuous FOV images. Then, the automated background removal and the optimal image selection procedures were sequentially conducted. Finally, the structural exterior map was constructed using the mesh-based image stitching method. To show the superiority of the proposed technique, the test results were compared with the raw digital image ones. Figure 5a,b show the target building and digital camera (L1D-20c of Hasselblad) mounted-UAV, Mavic 2 of DJI, used in this study. To evaluate the feasibility of the proposed technique, the UAV scanned along right-hand-side edge of the target structure, while the  
working distance of 4 m between the target ROI and UAV was kept. The digital images were obtained with the resolution of 3840 \(\times\) 2160 pixels and 30 frames per second in video format. Figure 6 shows the representative background removal results. The depth image of Figure 6b was obtained by using Monodepth2 from the raw FOV image of Figure 6a. Although the depth image contained the undesired noise components caused by scenery complexity such as window, target structure's texture and background objects, the ROI boundary between the target structure and the background objects was successfully extracted using Equation (1), as shown in Figure 6c. Then, the background region displayed in the right-side across the edge boundary on Figure 6c was removed, and the only ROI successfully remained as shown in Figure 6d. In order to quantitatively evaluate the accuracy of the depth image-based background removal results, a pixel-level error ratio between the resultant ROI image and its ground truth shown in Figure 6e was calculated. Figure 7 shows the error ratio results obtained from all the background removed-ROI images. The averaged error ratio along the entire ROI images turns out around 0.75%, which reveals that the proposed algorithm had over 99% accuracy for background removal and is acceptable for the subsequent structural exterior map construction. Next, the representative overlap ratio calculation results are shown in Figure 8. In Figure 8a, 14,421 and 13,850 numbers of image features were extracted from \(I_{1}\) and \(I_{7}\), respectively. The extracted features were initially matched as displayed in Figure 8a, and only inlier vectors were then remained as shown in Figure 8b. Subsequently, 66.49% of \(R\) was calculated using Equation (2) as shown in Figure 8b. Figure 5: Experimental setup: (**a**) Front view of target structure with region of interest and (**b**) digital camera mounted-UAV. Figure 6: Automated background removal results: (**a**) FOV image, (**b**) depth image, (**c**) ROI determination by boundary extraction, (**d**) resultant ROI image and (**e**) ground truth image of ROI. In order to properly stitch the digital images, it was generally recommended that \(R\) has greater than 50% [24]. In this study, \(R_{th}\) of 80% was used due to local image features such as window and target structure's texture, which were extremely repeated in the sequential image data and may increase the false feature matching. As shown in Figure 9, \(R\) straightforwardly decreased as \(q\) increased when \(p=1\), \(R\) becomes 82.9% and 77.2% corresponding to \(q=4\) and \(q=5\), respectively, as shown in Figure 9. Therefore, \(I_{4}\) was selected as the optimal image with respect to \(p=1\). Once \(I_{4}\) was selected, the next optimal image can be similarly selected from \(I_{4}\). This procedure was repeated until the entire optimal images were determined for structural exterior map establishment.