Abstract
Currently, unmanned aerial vehicles are increasingly being used in various construction projects such as housing developments, road construction, and bridge maintenance. If a drone is used at a road construction site, elevation information and orthoimages can be generated to acquire the construction status quantitatively. However, the detection of detailed changes in the site owing to construction depends on visual video interpretation. This study develops a method for automatic detection of the construction area using multitemporal images and a deep learning method. First, a deep learning model was trained using images of the changing area as reference. Second, we obtained an effective application method by applying various parameters to the deep learning process. The application of the time-series images of a construction site to the selected deep learning model enabled more effective identification of the changed areas than the existing pixel-based change detection. The proposed method is expected to be very helpful in construction management by aiding in the development of smart construction technology.
Highlights
The fields where drones can be applied are very diverse, such as terrain information construction, cadastral surveying, disaster management, environmental monitoring, inspection of various facilities, and exploration [1,2]
The field of construction involves the merging of design and construction information, visualization using the overlay of orthoimages and two-dimensional (2D) drawings, digital work, three-dimensional (3D) modeling and process comparison based on the process progress, construction quantity confirmation, and workload distribution; in this field, unmanned aerial vehicle (UAV) images and products are being used [3,4]
If drone technology is applied to the advanced payment tasks that are performed every month in construction projects, more accurate constructions can be achieved, and construction progress can be recorded through construction history management, which can be used for future maintenance or accident analysis
Summary
The fields where drones can be applied are very diverse, such as terrain information construction, cadastral surveying, disaster management, environmental monitoring, inspection of various facilities, and exploration (mineral and gas) [1,2]. A 2D or 3D design plan is developed for the corresponding monthly period, and the progress status chart is used for a time-series comparison of the corresponding work section. A change detection method that considers construction management has been developed using high-resolution UAV images acquired for road construction progress. The ground sampling distance (GSD) in a UAV photo is the most important setting factor in UAV photogrammetry for understanding the construction situation, and it is deeply related to the UAV flight altitude It is necessary for the appropriate GSD of an image to be identified according to the road facilities and for characteristics to be distinguished at the construction site. Even when there are differences in the camera, flight path, and acquisition geometry between images, the image is converted into the same field of view for more comprehensive use in the detection of changes
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