Abstract
Researchers at the University of Central Florida have developed
a method for reducing the time and cost of inspecting civil structures like
bridges and buildings. Conventional methods for visually assessing civil
infrastructures use subjectivity and may require long inspection time and high labor
costs. Although some technologies (such as robotic techniques, augmented
reality, and mixed reality interfaces) can collect objective, quantified data,
an inspector's expertise is still critical in many instances. Such
technologies, however, are not designed to work interactively with a human
inspector.
In contrast, the UCF Collective Intelligence Framework uniquely
blends AI with mixed reality (MR) and can be integrated into an MR-supported
see-through headset or a hand-held device. An inspector can analyze damage in
real time and calculate or assess its condition without performing any manual
measurements. At any step of the analysis/assessment, the inspector can intervene
and correct the operations of the AI. Another advantage of the system is that
the inspector can analyze defects in a remote location safely while reducing
the need for access equipment. For example, if the defect location is hard to
reach and poses a safety concern
(like under a bridge or atop high-rise buildings), the headset can zoom in and
still perform an assessment without needing equipment like a ladder. Consequently,
the methods can reduce inspection time and labor costs while ensuring a
quantified, reliable, and objective infrastructure evaluation with
human-verified results. The methodology is expandable for many types of
structures.
Technical Details
The UCF invention integrates unique AI detection and
segmentation algorithms into an MR framework that performs automatic detection
and segmentation of defect regions using real-time deep learning operations. In
infrastructure assessment, creating a large image dataset for machine learning is
essential but can be challenging. As a solution, the invention offers an
advanced data augmentation technique that enables the framework to generate a
synthetically sufficient number of images (like cracks and spalls) from
available image data.
In an example application, an inspector assesses a concrete
pier using an MR headset integrated with the framework. While the inspector
performs routine tasks, the AI system in the headset continuously guides the
inspector and shows possible defects in real time. Once the inspector confirms
a defect location, the AI system starts analyzing it by first executing defect
segmentation, then characterization to determine the specific type of defect.
If the defect boundaries need corrections or the segmentation requires fine-tuning,
the inspector can make the necessary adjustments. The system uses the alterations
made by the inspector to retrain the AI model so that the AI's accuracy improves
over time.
Partnering Opportunity
The research team is looking for partners to develop the
technology further for commercialization.
Stage of Development
Prototype available.
Benefit
Ensures reliable and objective infrastructure evaluation Reduces the need for access equipment by enabling inspectors to zoom in on far locations Allows effective use of data in infrastructure management systems AI continues learning and improves its accuracy over time Market Application
Transportation agencies Building and construction inspection
Brochure