3 May 2018 Automated, near real-time inspection of commercial sUAS imagery using deep learning
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Abstract
Commercial small Unmanned Aerial Systems (sUAS) have become popular for real-time inspection tasks due to their cost-effectiveness at covering large areas quickly. They can produce vast amounts of image data at high resolution, with little user involvement. However, manual review of this information can’t possibly keep pace with data collection rates. For time-sensitive applications, automated tools are required to locate objects of interest. These tools must perform at very low false alarm rates to avoid overwhelming the user. We approach real-time inspection as a semi-automated problem where a single user can provide limited feedback to guide object detection algorithms.
Conference Presentation
© (2018) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Chris Kawatsu, Chris Kawatsu, Ben Purman, Ben Purman, Aaron Zhao, Aaron Zhao, Andy Gillies, Andy Gillies, Mike Jeffers, Mike Jeffers, Paul Sheridan, Paul Sheridan, } "Automated, near real-time inspection of commercial sUAS imagery using deep learning", Proc. SPIE 10640, Unmanned Systems Technology XX, 1064005 (3 May 2018); doi: 10.1117/12.2304967; https://doi.org/10.1117/12.2304967
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