Chen, Kaiwen (2020) Analysis and management of UAV-captured images towards automation of building facade inspections. PhD thesis, Virginia Tech, USA.
Abstract
Building facades, serving mainly to protect occupants and structural components from natural forces, require periodic inspections for the detection and assessment of building façade anomalies. Over the past years, a growing trend of utilizing camera-equipped drones for periodical building facade inspection has emerged. Building façade anomalies, such as cracks and erosion, can be detected through analyzing drone-captured video, photographs, and infrared images. Such anomalies are known to have an impact on various building performance aspects, e.g., thermal, energy, moisture control issues. Current research efforts mainly focus on the design of drone flight schema for building inspection, 3D building model reconstruction through drone-captured images, and the detection of specific façade anomalies with these images. However, there are several research gaps impeding the improvement of automation level during the processes of building façade inspection with UAV (Unmanned Aerial Vehicle). These gaps are (1) lack effective ways to store multi-type data captured by drones with the connection to the spatial information of building facades, (2) lack high-performance tools for UAV-image analysis for the automated detection of building façade anomalies, and (3) lack a comprehensive management (i.e., storage, retrieval, analysis, and display) of large amounts and multi-media information for cyclic façade inspection. When seeking inspirations from nature, the process of drone-based facade inspection can be compared with caching birds' foraging food through spatial memory, visual sensing, and remarkable memories. This dissertation aims at investigating ways to improve the management of UAV-captured data and the automation level of drone-based façade anomaly inspection with inspirations from caching birds' foraging behavior. Firstly, a 2D spatial model of building façades was created in the geographic information system (GIS) for the registration and storage of UAV-images to assign façade spatial information to each image. Secondly, computational methods like computer vision and deep learning neural networks were applied to develop algorithms for automated extraction of visual features of façade anomalies within UAV-captured images. Thirdly, a GIS-based database was designed for the comprehensive management of heterogeneous inspection data, such as the spatial, multi-spectral, and temporal data. This research will improve the automation level of storage, retrieval, analysis, and documentation of drone-captured images to support façade inspection during a building's service lifecycle. It has promising potential for supporting the decision-making of early-intervention or maintenance strategies to prevent façade failures and improve building performance.
| Item Type: | Thesis (Doctoral) |
|---|---|
| Thesis advisor: | Reichard, Georg; Moore, Ignacio T; Kim, Mintai and Akanmu, Abiola Abosede |
| Uncontrolled Keywords: | façade inspection; geographic information system; computer vision; deep learning; machine learning |
| Index terms: | drone, computer vision, documentation, building performance, strategy, neural network, decision-making, machine learning, reconstruction, database, deep learning, inspection, media, lifecycle, periodical, unmanned aerial vehicle, automation, dissertation, geographic information system |
| Subjects: | management, professional development, data management, sociology, decision analysis, artificial intelligence, project delivery, automation and robotics, computer vision, geographical techniques and analysis, building construction, quality assurance, research dissemination and communication |
| Topics: | Research Practice, Information Management, Business Strategy, Digital Applications, Project Management, Geographical Context, Engineering Principles, Risk Management, Quality Management |
| Descriptive scope: | 2 PC |
N.B. Descriptive scope is a count of how many of the five facets of empirical research are indicated by the words used in title, abstract and keywords. It is not intended as a judgement on the research; merely a count of the kind of word we would expect to indicate Phenomenon, Concepts, Theoretical framing, Empirical techniques, Analytical techniques. If all five are present, then a code of “5 PCTEA” will indicate this. If you feel the coding for this record is questionable, we welcome discussion around the terms we matched or the way we categorized them. The facet you would expect may not be coded, or a facet may be coded inappropriately. This can also bear on a larger question, of which facets should be treated as defining in construction management research. Please get in touch, and we will look at it. More details here