# Automated 3D Image Processing System for Inspection of Residential Wall Spalls
## 1 Introduction  
As people become more safety-conscious, the demands placed on buildings are increasing. Concrete infrastructures that have been in service for decades are often plagued by overloading and material deterioration. Structural health assessments, focusing on wall spalling, are essential for evaluating concrete structures' health and ensuring their serviceability. Wall spalls can lead to failure of the concrete cover and risk of rusting of the internal steel reinforcement, which may lead to corrosion and damage of the internal structure of the wall as well as instability of the building structure, thus affecting the overall safety of the building [1]. If not repaired and reinforced in time, it may lead to wall collapse or other serious accidents, jeopardizing the lives of people inside and outside the building. Traditional inspection methods rely on manual labor, requiring the inspector to visually assess the condition of the structure, identifying wall defects with the naked eye. These methods can be labor-intensive and time-consuming given the diverse environments and complex areas and are highly susceptible to human error. To cut costs and risks in inspections while boosting accuracy, researchers are increasingly using unmanned vehicles (unmanned aerial vehicle/unmanned ground vehicle) for data collection [2]. Currently,UAV is mainly used for outdoor building inspections. [PERSON] et al. [3] proposed a method for bridge defect inspection using UAV for automated data acquisition, which provided high-speed, safe, and effective inspections of bridge defects. [PERSON] et al. [4] used UAV to perform outdoor 3D reconstruction based on structure from motion (SfM) and proposed an adaptive crack segmentation model. Although the above studies have achieved good results in automated detection, they impose strict environmental requirements, and due to the flight characteristics of UAVs, researchers face great operational challenges in narrow and complex environments. Thus, self-navigating UCVs are preferred for data acquisition in complex civilian building scenes. [PERSON] et al. [5] proposed a 3D vision technique for crack damage identification, wherein a robot autonomously collects building surface crack information. [PERSON]. [PERSON] et al. [6] proposed a new approach that integrates simultaneous localization and mapping (SLAM) algorithms with robot motion control and pathfinding to collect building information. In summary, UAVs and UGVs have been widely used in defect detection [7]. Compared to manual inspection methods, these automated technologies enable the completion of large-scale data collection, providing high-resolution images and precise geographic location information in a shorter time. Thus, the inspection efficiency is improved, and the accuracy and reliability of defect detection are significantly higher. The collected data will be used for spall disease detection. Currently, defect detection mainly involves 2D and 3D model-based target recognition and segmentation [8]. [PERSON] et al. [9], aiming at dynamically capturing defect characteristics based on shape attributes, utilized DCN and multi-task ensemble learning techniques. A Faster RCNN-based approach to automatically detect concrete spalls was developed by [PERSON] et al. [10]. An affordable depth camera was used in this study for integrated detection, localization, and quantification of spall damage. In addition, [PERSON] et al. [11] introduced an ensemble learning approach incorporating morphological attention to detect surface defects at the bounding-box level, specifically targeting spalls. The images captured by the above methods typically represent only a small portion of the entire scene. This limited perspective presents a challenge for efficiently recognizing damages on wall surfaces in large scenes.