Automatic Detection of Earthquake-Damaged Buildings by Integrating UAV Oblique Photography and Infrared Thermal Imaging

Abstract

Extracting damage information of buildings after an earthquake is crucial for emergency rescue and loss assessment. Low-altitude remote sensing by unmanned aerial vehicles (UAVs) for emergency rescue has unique advantages. In this study, we establish a remote sensing information-extraction method that combines ultramicro oblique UAV and infrared thermal imaging technology to automatically detect the structural damage of buildings and cracks in external walls. The method consists of four parts: (1) 3D live-action modeling and building structure analysis based on ultramicro oblique images; (2) extraction of damage information of buildings; (3) detection of cracks in walls based on infrared thermal imaging; and (4) integration of detection systems for information of earthquake-damaged buildings. First, a 3D live-action building model is constructed. A multi-view structure image for segmentation can be obtained based on this method. Second, a method of extracting information on damage to building structures using a 3D live-action building model as the geographic reference is proposed. Damage information of the internal structure of the building can be obtained based on this method. Third, based on analyzing the temperature field distribution on the exterior walls of earthquake-damaged buildings, an automatic method of detecting cracks in the walls by using infrared thermal imaging is proposed. Finally, the damage information detection and assessment system is researched and developed, and the system is integrated. Taking earthquake search-and-rescue simulation as an example, the effectiveness of this method is verified. The damage distribution in the internal structure and external walls of buildings in this area is obtained with an accuracy of 78%.

UAV Framework for Autonomous Onboard Navigation and People/Object Detection in Cluttered Indoor Environments

“…Reported key areas on the use of UAVs post-disasters include aerial monitoring of damage evaluation, localisation of victims, SAR logistics and cargo delivery [8,9]. UAVs have also assisted through the rapid post-disaster assessment of damaged buildings after an earthquake [10,11], the custom design of defibrillator payloads [12,13] and the deployment of first aid kits in remote areas [14]. Recent research has also showed how UAVs can provide fast assessments on the identification of victims and their conditions.…”

Trends, benefits, and barriers of unmanned aerial systems in the construction industry: a survey study in the United States

“…UAS imagery also provided detailed information about damaged structures, their structural material and components, as well as their failure mechanism (Adams et al 2014). Recently, Zhang et al (2020) proposed a four-step automatic building damage identification and information extraction technique using UAS-acquired oblique thermal images. After testing their system on earthquake-affected buildings, the authors advocated the usage of UASs for post-disaster assessment and showed that their method provided a damage detection accuracy of around 80%.…”

Underexposed Vision-Based Sensors’ Image Enhancement for Feature Identification in Close-Range Photogrammetry and Structural Health Monitoring

“…Histogram equalization [23,24] was used to enhance image gray resolution for crack detection [25] and crack monitoring from thermal imaging [26]. Wavelet transforms [27,28] were used to correct vision-based images for damage and crack detection [29,30] and fatigue crack detection [31]. Contrast enhancement was conducted on vision-based images to separate the crack and the background area [32], and the advanced deep learning method was capable of autonomously detecting concrete cracking, steel corrosion, and delamination [33].…”