Tests conducted by Cornell and the U.S. Navy have used new algorithms to outperform state-of-the-art programming for autonomous underwater sonar imaging, significantly improving the speed and accuracy for identifying objects such as explosive mines, sunken ships, airplane black boxes, pipelines and corrosion on ship hulls.
Newly developed algorithms raise the bar for autonomous underwater imaging
