Deep Learning-Based Automated Background Removal for Structural Exterior Image Stitching

This paper presents a deep learning-based automated background removal technique for structural exterior image stitching. In order to establish an exterior damage map of a structure using an unmanned aerial vehicle (UAV), a close-up vision scanning is typically required. However, unwanted background objects are often captured within the scanned digital images. Since the unnecessary background objects often cause serious distortion on the image stitching process, they should be removed. In this paper, the automated background removal technique using deep learning-based depth estimation is proposed. Based on the fact that the region of interest has closer working distance than the background ones from the camera, the background region within the digital images can be automatically removed using a deep learning-based depth estimation network. In addition, an optimal digital image selection based on feature matching-based overlap ratio is proposed. The proposed technique is experimentally validated using UAV-scanned digital images acquired from an in-situ high-rise building structure. The validation test results show that the optimal digital images obtained from the proposed technique produce the precise structural exterior map with computational cost reduction of 85.7%, while raw scanned digital images fail to construct the structural exterior map and cause serious stitching distortion.

Automated summary

This paper proposes a deep learning-based technique for automatically removing background objects from digital images for structural exterior image stitching, achieving a computational cost reduction of 85.7% and generating precise structural exterior maps.