No
Yes
View More
View Less
Working...
Close
OK
Cancel
Confirm
System Message
Delete
Schedule
An unknown error has occurred and your request could not be completed. Please contact support.
Scheduled
Wait Listed
Personal Calendar
Speaking
Conference Event
Meeting
Interest
Schedule TBD
Conflict Found
This session is already scheduled at another time. Would you like to...
Loading...
Please enter a maximum of {0} characters.
Please enter a maximum of {0} words.
must be 50 characters or less.
must be 40 characters or less.
Session Summary
We were unable to load the map image.
This has not yet been assigned to a map.
Search Catalog
Reply
Replies ()
Search
New Post
Microblog
Microblog Thread
Post Reply
Post
Your session timed out.
This web page is not optimized for viewing on a mobile device. Visit this site in a desktop browser to access the full set of features.
2017 GTC San Jose

S7732 - Deep Learning for Condition Assessment of Civil Infrastructure Systems

Session Speakers
Session Description

We'll present the use of deep learning for autonomous condition assessment of civil infrastructure systems. Regular inspection of civil infrastructure systems is crucial for safe operations. Manual inspection is currently the predominant method of inspection and is time-consuming, tedious, and subjective. A less time-consuming and inexpensive alternative is the use of optical instrumentation (for example, digital cameras), where the feasibility of using image processing techniques to detect deterioration in structures has been acknowledged by leading experts in the field. Due to the recent advances in using CNNs, the vision-based classification performance of computers has been improved significantly. A CNN learns the appropriate classification features that in traditional algorithms were hand-engineered. Eliminating the need for dependence on prior knowledge and human effort in designing features is a major advantage of CNNs. We'll discuss CNN-based approaches for condition assessment of infrastructure systems, including a new framework that combines deep convolutional neural network and Naive Bayes classifier to detect cracks in videos. The crack patches are spatially and temporally clustered and the posterior probabilities of being real cracks are derived. Experimental tests have been carried out to evaluate the performance of the proposed system.


Additional Session Information
All
Talk
Computer Vision and Machine Vision Deep Learning and AI
Architecture / Engineering / Construction
25 minutes
Session Schedule