Defining Structural Cracks in Exterior Walls of Concrete Buildings Using an Unmanned Aerial Vehicle

Abstract

In Republic of Korea, cracks in concrete structures are considered to be objective structural defects, and the constant maintenance of deteriorating facilities leads to substantial social costs. Thus, it is important to develop technologies that enable economical and efficient building safety inspection. Recently, the application of UAVs and deep learning is attracting attention for efficient safety inspection. However, the currently developed technology has limitations in defining structural cracks that can seriously affect the stability of buildings. This study proposes a method to define structural cracks on the outer wall of a concrete building by merging the orthoimage layer and the structural drawing layer with the UAV and deep learning that were previously applied during a safety inspection. First, we acquired data from UAV-based aerial photography and detected cracks through deep learning. Structural and non-structural cracks were defined using detected crack layer, design drawing layer defined the structural part, and the orthoimage layer was based on UAV images. According to the analysis results, 116 structural parts cracks and 149 non-structural parts cracks were defined out of a total of 265 cracks. In the future, the proposed method is expected to greatly contribute to safety inspections by being able to determine the quality and risk of cracks.

Localization of Cracks in Concrete Structures Lacking Reference Objects and Feature Points Using an Unmanned Aerial Vehicle

“…The resolution of an aerial image is influenced by the performance of the mounted camera sensor and the shooting distance. Woo et al [24] used an unmanned aerial vehicle with a resolution of 20 Megapixels (MP) to define structural cracks in the exterior walls of concrete buildings. Aerial images were acquired by setting the shooting distance to 2 m. Jeong et al [25] conducted crack detection using aerial images with a resolution of 12 MP taken from a shooting distance of 5 to 10 m. However, they confirmed that the detection had low accuracy and a limited detection range.…”