Bridge Crack Detection Using Multi-Rotary Uav and Object-Base Image Analysis

Abstract

ABSTRACT:Bridge is an important infrastructure for human life. Thus, the bridge safety monitoring and maintaining is an important issue to the government. Conventionally, bridge inspection were conducted by human in-situ visual examination. This procedure sometimes require under bridge inspection vehicle or climbing under the bridge personally. Thus, its cost and risk is high as well as labor intensive and time consuming. Particularly, its documentation procedure is subjective without 3D spatial information. In order cope with these challenges, this paper propose the use of a multi-rotary UAV that equipped with a SONY A7r2 high resolution digital camera, 50 mm fixed focus length lens, 135 degrees up-down rotating gimbal. The target bridge contains three spans with a total of 60 meters long, 20 meters width and 8 meters height above the water level. In the end, we took about 10,000 images, but some of them were acquired by hand held method taken on the ground using a pole with 2-8 meters long. Those images were processed by Agisoft PhotoscanPro to obtain exterior and interior orientation parameters. A local coordinate system was defined by using 12 ground control points measured by a total station. After triangulation and camera self-calibration, the RMS of control points is less than 3 cm. A 3D CAD model that describe the bridge surface geometry was manually measured by PhotoscanPro. They were composed of planar polygons and will be used for searching related UAV images. Additionally, a photorealistic 3D model can be produced for 3D visualization. In order to detect cracks on the bridge surface, we utilize object-based image analysis (OBIA) technique to segment the image into objects. Later, we derive several object features, such as density, area/bounding box ratio, length/width ratio, length, etc. Then, we can setup a classification rule set to distinguish cracks. Further, we apply semi-global-matching (SGM) to obtain 3D crack information and based on image scale we can calculate the width of a crack object. For spalling volume calculation, we also apply SGM to obtain dense surface geometry. Assuming the background is a planar surface, we can fit a planar function and convert the surface geometry into a DSM. Thus, for spalling area its height will be lower than the plane and its value will be negative. We can thus apply several image processing technique to segment the spalling area and calculate the spalling volume as well. For bridge inspection and UAV image management within a laboratory, we develop a graphic user interface. The major functions include crack auto-detection using OBIA, crack editing, i.e. delete and add cracks, crack attributing, 3D crack visualization, spalling area/volume calculation, bridge defects documentation, etc.

UAV Photogrammetry for Concrete Bridge Inspection Using Object-Based Image Analysis (OBIA)

“…The purpose of this algorithm is to emphasize or improve the contrast to facilitate the extraction of information which is not evident in the original images, in order to support the image analysis operations [4]. In this case study, for contrast enhancement, linear stretch algorithm was used instead of non-linear improvement methods [43], according to Rau et al [18].…”

“…Segments are vector polygons [48]. This process is bottom-up: it starts from a pixel to join other similar pixels to become an object until predefined homogeneity criteria are reached [18]. It is important to underline that the segments, composed by many pixels, have additional spectral information compared to the individual pixels (like average, minimum and maximum values, variance and so on).…”

“…An interesting investigative campaign was conducted using digital terrestrial and UAV photogrammetry on the Chia-nan concrete bridge (Tainan, Taiwan) [18]. The study aimed to identify deteriorations (such as spalling concrete) and cracking frameworks present on reinforced concrete bridge surfaces.…”

Photogrammetric Technology for Remote High-Precision 3d Monitoring of Cracks and Deformation Joints of Buildings and Constructions

“…In recent years, more and more research and development activities are devoted to the use of the photogrammetry methods. However, a large number of studies are focused on the use of photogrammetric techniques for the detection and measurement of cracks (Hampel, Maas, 2009;Rau et al, 2017;Sarker et al, 2017;Valença et al, 2013;Zheng, 2014), and only a small number of investigators consider the use of photogrammetric approach for the crack monitoring purposes. Some proposed solutions (GEOSEC, 2019) provide 0.1 mm accuracy, and appears as an essential replacement for scalebased and mechanical sensors, while others provide 0.01 mm accuracy, but require photographing in tight to the object (WELTWAVE, 2019).…”

MAVNet: An Effective Semantic Segmentation Micro-Network for MAV-Based Tasks

“…While moving complex processing to the cloud is a common solution for constrained edge devices, it inhibits the MAV's operations in remote areas or subterranean regions where wireless connectivity is often not available. Examples of such applications include relative visual localization of other robots in multi-robot systems [15], semantic mapping [16], and damage detection for infrastructure inspection [17], [18]. There are some recent research in deep learning inference at the edge [1], [19].…”