## 1 Introduction
The number of clusters in the histogram was determined by analyzing the histogram of the original image, and the K-means algorithm [19] was used as the clustering method. Histogram equalization and stretching were selectively applied by comparing the histogram cluster range and ratio of the number of pixels in the cluster to the clustered images. A method to overcome the limitations of UAVs is to combine image processing and 3D modeling. Accurately identifying the safety status of temporary structures is limited owing to the low resolution of the images captured by UAVs of the entire building in one sheet [20]. Three-dimensional (3D) modeling can supplement the limits of the inspection area identified by a flat image. Image registration is a process that determines a corresponding relationship when there is a part in which the same object appears in different images. In the field of computer vision, it is easy to find a corresponding relationship if two given images are nearly identical. However, if various transformations are provided between the two images, it is difficult to find a corresponding relationship [21; 22; 23]. To solve this problem, we utilized a method for separating the ground and building areas via a planarity analysis using an UAV-based point cloud that can be generated at a low cost and can automatically create a building model. This technique performs a planarity analysis using the generated point cloud, selecting a ground plane, removing the ground from the point cloud, and generating an orthographic projection image using the point cloud and original point cloud [24]. Using the orthographically projected image, building candidates were selected via the outline detection algorithm, and non-buildings were excluded from the candidates using the building conditions. Finally, a 3D building model was created by converting the selected building area into 3D ground coordinates and specifying the height value. Considering research related to safety nets, most of the research regarding safety nets involves comparative studies of regulations related to safety nets [25; 26]. Research on using UAVs to prevent safety accidents at construction sites is increasing, but most research areas are 3D modeling [27; 28], safety inspections [29; 30], and monitoring unsafe working conditions using deep learning [31; 32]. Even in research on obstacles that make it difficult to apply UAVs to construction sites, there is little research that mentions problems caused by safety nets installed on temporary structures [33]. To the best of our knowledge, no research has been conducted regarding the internal observation methods for the construction of safety nets. This study contributes to the reduction in accidents at construction sites by proposing a technology that can observe the internal objects inside construction safety nets installed at construction sites. The purpose of this study was to propose an observation method that can identify the internal objects inside a temporary structure covered with a construction safety net using an image processing technique and 3D modeling. The proposed method can be helpful in inspecting temporary structures by observing changes in their installation status, dismantling of connections, and damage during various operations that occur after net installation. This is performed by comparing the image before installing a construction safety net and the image processed through the proposed method after safety net installation. When applying temporary structure safety inspection technology using AI, images taken before construction safety net installation can serve as a guide when observing image processed images after net installation.