Drones for Utilities: How AI is Redefining Utility Inspections

How AI and machine learning algorithms redefine utility inspections as society faces this pandemic.

The following is a guest post by Jaro Uljanovs, Lead AI Developer and Data Scientist at Sharper Shape, specialists in automated industrial inspections.

Artificial intelligence (AI) boasts a wide range of potential applications, across nearly every industry imaginable — healthcare, automotive, retail, even fast food. But it is the utility industry where AI and machine learning (ML) are beginning to demonstrate some of their most impactful effects on many aspects of the business. Power companies are increasingly leaning on AI to improve their electricity delivery an– in places like the Amazon and California – prevent potential wildfires through drone management software and vegetation management. In a post-COVID world where a reduced on-site workforce is quickly becoming the norm, AI is actually enhancing human jobs.

From data collection and analysis to the presentation of actionable insights, AI and ML algorithms are quickly redefining how utility companies manage their electric infrastructure.

Consolidating and classifying data

Utility companies oversee massive infrastructure networks, comprising poles, conductors, substations. Transmission and distribution lines which contain these crucial components,       span thousands of miles. Vegetation management around this key infrastructure must also be monitored, as it presents a danger of fire or outage.

Taking a comprehensive snapshot of these assets means utilizing a variety of different sensors for powerline inspections. These sensors include light detection and ranging (LiDAR), color (RGB), hyperspectral and thermal imagery.

This allows the drone mapping software to capture everything — from vegetation proximity, to infrastructure assets, to individual components (such as insulators on transformers) and their operational integrity, to hot spots indicating potential fire risks.

That is a lot of data to capture, catalog and process. And there are a lot of individual elements within that data — even in just one image — to pinpoint and classify, let alone do so accurately. Classifying billions of data points across all those sensors is an impossibly time-consuming task to do manually.

AI and ML tools can accomplish that same work — scanning thousands of images collected across thousands of miles of utility infrastructure — in seconds. LiDAR point cloud segmentation can detect conductors (quite a difficult component-type to segment) with an accuracy of over 95% for each individual point, while hyperspectral image segmentation can identify vegetation species with an accuracy of up to 99%.

More than that, when paired with drone

This post was originally published by Drone Life on . Please visit the original post to read the complete article.

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