AR-integrated intelligent visual inspection system for health monitoring of constructed facilities

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The aging of constructed facilities, such as buildings, bridges, pipelines, etc., is a critical issue worldwide, as it affects the safety of the facilities. In Canada, many constructed facilities, especially infrastructure facilities, are in sparsely populated or indigenous areas, and the development of an automated intelligent visual inspection system that can greatly reduce manpower requirement and enhance inspection efficiency and effectiveness is necessary. In this regard, the goal of this research program is to develop near-real-time (NRT) fast defect recognition algorithms and utilize augmented reality (AR) to develop an AR-integrated intelligent visual inspection system for near-real-time health monitoring of constructed facilities. This intelligent system will comprise two modes: (1) automated drone inspection mode (M1); and (2) in-person inspection mode (M2). For M1, a GPS-guided drone will fly along a pre-planned route to automatically capture facility surface inspection data (e.g., videos or photos), and display them on a tablet or smartphone in real time, followed by immediate superimposing of recognized defects on the displayed view (i.e., AR effects). Whenever a defect is identified, the corresponding GPS position will be recorded. After the drone finishes the entire planned route, it will automatically fly back to each of the recorded positions to capture more detailed data around the spotted defect. For M2, an AR-HMD (head-mounted display) will capture inspection data and transmit them to a laptop for fast defect recognition, followed by superimposing recognized defects on AR-HMD. Interactive gesture control of AR-HMD will be incorporated in the development. To achieve the goal of this program, four research objectives are set: (1) to develop near-real-time fast defect recognition algorithms for steel and reinforced concrete (RC) constructed facilities; (2) to integrate a drone with tablets/smartphones of both iOS/iPadOS and Android systems for the M1 mode; (3) to integrate an AR-HMD and its gesture control with a laptop for the M2 mode; and (4) to design and develop user-friendly AR user interfaces for both modes. Nine Highly Qualified Personnel (HQP) will be trained in this program, including 3 PhD students, 4 MASc students, and 2 undergraduate research assistants (URAs). Nowadays, the AR applications in the Architecture, Engineering and Construction (AEC) industry mostly rely on pre-loaded data on the AR equipment for object or pattern recognition. This program will bring AR applications to the next level by processing newly captured data in a near-real-time manner, which leads to the importance of the fast defect recognition algorithms to be developed. At the same time, the proposed automated drone inspection mode and the in-person inspection mode with interactive AR-HMD may both revolutionize the current inspection practices and significantly benefit the public and private engineering sectors in Canada and beyond.