No abstract

Active research on crack detection technology for structures based on unmanned aerial vehicles (UAVs) has attracted considerable attention. Most of the existing research on localization of cracks using UAVs mounted the Global Positioning System (GPS)/Inertial Measurement Unit (IMU) on the UAVs to obtain location information. When such absolute position information is used, several studies confirmed that positioning errors of the UAVs were reflected and were in the order of a few meters. To address these limitations, in this study, without using the absolute position information, localization of cracks was defined using relative position between objects in UAV-captured images to significantly reduce the error level. Through aerial photography, a total of 97 images were acquired. Using the point cloud technique, image stitching, and homography matrix algorithm, 5 cracks and 3 reference objects were defined. Importantly, the comparative analysis of estimated relative position values and ground truth values through field measurement revealed that errors in the range 24–84 mm and 8–48 mm were obtained on the x- and y-directions, respectively. Also, RMSE errors of 37.95–91.24 mm were confirmed. In the future, the proposed methodology can be utilized for supplementing and improving the conventional methods for visual inspection of infrastructures and facilities.

Asbestos is a class 1 carcinogen, and it has become clear that it harms the human body. Its use has been banned in many countries, and now the investigation and removal of installed asbestos has become a very important social issue. Accordingly, many social costs are expected to occur, and an efficient asbestos investigation method is required. So far, the examination of asbestos slates was performed through visual inspection. With recent advances in deep learning technology, it is possible to distinguish objects by discovering patterns in numerous training data. In this study, we propose the use of drone images and a faster region-based convolutional neural network (Faster R-CNN) to identify asbestos slates in target sites. Furthermore, the locations of detected asbestos slates were estimated using orthoimages and compiled cadastral maps. A total of 91 asbestos slates were detected in the target sites, and 91 locations were estimated from a total of 45 addresses. To verify the estimated locations, an on-site survey was conducted, and the location estimation method obtained an accuracy of 98.9%. The study findings indicate that the proposed method could be a useful research method for identifying asbestos slate roofs.

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.