Keeping a nation’s lights on means constantly inspecting electricity poles for damage. Before EDP partnered with DefinedCrowd, teams of specialists had to jump into helicopters, survey and photograph poles from way up in the sky and use those photographs to manually fill out damage reports when the day was done.
That’s a slow, expensive process with a lot of moving parts to answer a simple question: “which of our poles need fixing?”
As a proof-of-concept, we delivered data that would train a model to identify electricity poles and their components (insulators and crossbars). That meant gathering 12,500 images — 7,000 with poles — that were then annotated by our highly-skilled crowd (200,000+ strong). We used 3,000 of those images to train a pole detection model and 500 more to test it.
Next, we collected data that would train models to detect damage by hand-selecting a subset of 900 high-resolution images to send to our crowd. They identified the type of damage in each image. We used those annotated images to train and test one model to keep track of damaged or missing insulators and another to detect corrosion on crossbars.
With these models, EDP is on the path to faster, cheaper, and more accurate asset performance management processes (APM). In the short-term, that means drones will feed high-quality images of poles into automated systems that will deliver comprehensive damage reports as a result. In the long-term, automated APM will allow EDP to ask and answer more sophisticated maintenance questions. Put simply, instead of reacting to already damaged poles, the automated APM will allow EDP to proactively identify which poles will need fixing in the future.