mlsanityData to 14 Oct 2026

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

arXiv 2103.01426 ↗First posted 2 Mar 2021cs.LG cs.AIcited by 0

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Abstract (arXiv)

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 ~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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 15 of its 39 references that are in the map. First posted on arXiv 2 Mar 2021.

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15 of 39 references.

References · 15

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3 more
  1. 2018Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoderscited by 264
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