AdeNet: Deep learning architecture that identifies damaged electrical insulators in power lines
This work addresses the need for automated monitoring of power line insulators to reduce labor and provide early warnings, though it is incremental as it builds on existing deep learning methods.
The paper tackled the problem of identifying damaged electrical insulators in power lines using UAV-collected images, achieving an accuracy of 88.8% and reducing the false negative rate to approximately 7% with AdeNet, a deep neural network.
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.