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Ceramic insulators are important to electronic systems, designed andinstalled to protect humans from the danger of high voltage electric current.However, insulators are not immortal, and natural deterioration can graduallydamage them. Therefore, the condition of insulators must be continuallymonitored, which is normally done using UAVs. UAVs collect many images ofinsulators, and these images are then analyzed to identify those that aredamaged. Here we describe AdeNet as a deep neural network designed to identifydamaged insulators, and test multiple approaches to automatic analysis of thecondition of insulators. Several deep neural networks were tested, as wereshallow learning methods. The best results (88.8\%) were achieved using AdeNetwithout transfer learning. AdeNet also reduced the false negative rate to$\sim$7\%. While the method cannot fully replace human inspection, its highthroughput can reduce the amount of labor required to monitor lines for damagedinsulators and provide early warning to replace damaged insulators.

AdeNet: Deep learning architecture that identifies damaged electrical insulators in power lines