Automated, near real-time inspection of commercial sUAS imagery using deep learning
Chris Kawatsu, Benjamin Purman, Aaron Zhao, Andy Gillies, Mike Jeffers, Paul J. Sheridan · 2018
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.