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Scientists Develop Mobile System for Object Detection, Image Analysis in Disaster Response

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ORNL researchers have collected images of damage following extreme weather events such as Hurricane Ian to build a robust damage system that can detect and analyze utility pole damage to aid in disaster response efforts.

Credit: ORNL, U.S. Dept. of Energy

A prototype system developed by researchers at the U.S. Department of Energy's Oak Ridge National Laboratory (ORNL) can detect and assess utility pole damage following natural disasters.

The system runs on drone-mounted edge computing hardware and uses machine learning algorithms and onboard imaging hardware to perform damage assessments.

The data is uploaded to the Environment for Analysis of Geo-Located Energy Information (EAGLE-I) central processing hub.

EAGLE-I, a real-time situational awareness tool, also can be used to monitor energy infrastructure assets, report energy outages, display potential threats to energy infrastructure, and coordinate emergency response and recovery.

ORNL's David Hughes said, "This pole detection project is just our first step into 'AI [artificial intelligence] on the edge.' Our intent is to expand into multiple observables — substations, for example — and be able to classify them as damaged or undamaged infrastructure."

From Oak Ridge National Laboratory
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Abstracts Copyright © 2023 SmithBucklin, Washington, DC, USA


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