  Ceramic insulators are important to electronic systems, designed and
installed to protect humans from the danger of high voltage electric current.
However, insulators are not immortal, and natural deterioration can gradually
damage them. Therefore, the condition of insulators must be continually
monitored, which is normally done using UAVs. UAVs collect many images of
insulators, and these images are then analyzed to identify those that are
damaged. Here we describe AdeNet as a deep neural network designed to identify
damaged insulators, and test multiple approaches to automatic analysis of the
condition of insulators. Several deep neural networks were tested, as were
shallow learning methods. The best results (88.8\%) were achieved using AdeNet
without transfer learning. AdeNet also reduced the false negative rate to
$\sim$7\%. While the method cannot fully replace human inspection, its high
throughput can reduce the amount of labor required to monitor lines for damaged
insulators and provide early warning to replace damaged insulators.
