  Power lines are dangerous for low-flying aircraft, especially in
low-visibility conditions. Thus, a vision-based system able to analyze the
aircraft's surroundings and to provide the pilots with a "second pair of eyes"
can contribute to enhancing their safety. To this end, we have developed a deep
learning approach to jointly detect power line cables and pylons from images
captured at distances of several hundred meters by aircraft-mounted cameras. In
doing so, we have combined a modern convolutional architecture with transfer
learning and a loss function adapted to curvilinear structure delineation. We
use a single network for both detection tasks and demonstrated its performance
on two benchmarking datasets. We have integrated it within an onboard system
and run it in flight, and have demonstrated with our experiments that it
outperforms the prior distant cable detection method on both datasets, while
also successfully detecting pylons, given their annotations are available for
the data.
