## 1 Introduction  
Various methods have been employed to prevent accidents at construction sites, among which, the use of unmanned aerial vehicles (UAVs) as safety management tools at construction sites is increasing [1, 2]. One of the areas where UAVs are likely to be used at construction sites is for the safety management of temporary structures. During the construction process, it is important to check the structural defects of the temporary structure and the working conditions in the temporary structure, such as scaffolding, supports, and climbing formworks. Site managers apparently have trouble managing the installation condition of temporary structures and the unsafe behavior of workers, especially for large construction sites and high-rise buildings because access and movement are time-consuming. If UAVs can be used to inspect temporary structures attached to the exterior of buildings, site managers can efficiently manage the temporary structures. However, when observing the inside of a temporary structure from the outside using UAVs, it is difficult to determine the accuracy of the installation condition of the structural members and the condition of workers wearing personal protective equipment, owing to the attachment of the construction safety net, which should be installed on the outer surface of the temporary structure in the form of a net and fixed to the temporary structure members to prevent workers from falling. Thus, the utilization of UAVs is limited because the safety net acts as an obstacle for viewing the internal object from the images obtained by UAVs. As far as the authors know, there is no method available to observe the inside of a temporary structure covered with a construction safety net. The only existing method is to visually observe the image of the temporary structure covered with the construction safety net, but the presence of the safety net makes it impossible to clearly observe the inside of the temporary structure. To solve this problem, a new approach based on an image processing technique that can reduce the blocking phenomenon of the construction safety net is necessary. Various studies have been conducted on methods utilizing images obtained from UAVs, in particular in the fields of construction sites, military, disaster management, environmental monitoring, digital mapping, and orthographic image production [3; 4; 5; 6; 7; 8; 9; 10]. The use of UAVs and 3D scanning is also increasing in the field of cultural heritage and archeology, where precise data and detailed work are required [11; 12; 13]. Specifically, an UAV image-based facility inspection method [14] was proposed as an alternative to the conventional visual inspection method to efficiently inspect aging facilities. In this method, a reference image database is built based on images captured using an UAV, and the newly acquired images are automatically processed to identify and check for the deformed parts on the target surface of the facility. The applicability of UAV imaging for surveying construction sites and confirming their compliance with installation standards for temporary structures to improve safety management at construction sites was also studied [15]. Owing to the advantages of UAV image acquisition, it is possible to quickly document the condition of a construction site and measure it without interfering with the work of field personnel. UAV imaging enables the study of image segmentation, character/face recognition, and extraction of meaningful areas from images. These image processing techniques are widely used in the architecture, medicine, military, and meteorological fields [16; 17]. Recently, to overcome the limitations of the existing contrast enhancement techniques, an image contrast enhancement technique based on a clustering method, such as histogram stretching and histogram equalization, was proposed [18] because image contrast enhancement plays an important role in image processing.