Sharper Shape's drones run computer vision to spot utility hazards. Labelbox produced the image signal and a model-assisted loop, cutting labeling cost 50% and speeding training 10x.

The challenge

Sharper Shape builds technology for safe, efficient utility transmission and distribution, using drones to inspect power infrastructure. Its computer vision models, running in advanced aerial sensor systems, automatically collect and analyze inspection data — flagging dangerous setups like vegetation growing too close to wires or broken insulators so utilities can address hazards. Training multiple computer vision models takes a vast amount of accurately labeled imagery. Before Labelbox, the team relied on heavily manual workflows and open-source tools that lacked the configuration and support it needed.

The approach

Sharper Shape used Labelbox to produce the signal and streamline its pipeline, connecting raw data via a simple API and working across data types like tiled imagery in one place. Collaboration features let internal and external contributors onboard, train, and produce signal together in one centralized environment. The team then accelerated with model-assisted labeling — importing its model into Labelbox and focusing effort on edge cases.

With the streamlined design of Labelbox, we are able to cut costs on labeling by as much as 50% while maintaining the highest quality in our training data, and get to training our models faster. With human-in-the-loop model-assisted labeling, we expect another huge reduction in time and costs to the labeling process. After a preliminary model is trained, we can run a loop to generate labels from our model’s inference, and feed those back into Labelbox, effectively cutting the labeling load of our labelers to that of reviewing for false positives. That allows us to increase our capabilities and model accuracies exponentially with respect to time for the amount of components and defects we can detect and classify.

— Edward Kim, Data Analyst / AI at Sharper Shape

Before using Labelbox, we struggled with managing our training data creation infrastructure and manual experiment tracking. We’re now able to concentrate on model building and deployment, without sparing engineering effort and are able to speed up model training by over 10X.

— Jaro Uljanovs, Data Science / AI at Sharper Shape

The outcome

Sharper Shape cut labeling costs by as much as 50% while maintaining the highest-quality signal, and sped up model training by over 10X — freeing the team to focus on model building and deployment. With a human-in-the-loop, model-assisted loop, contributors increasingly review for false positives instead of labeling from scratch, expanding the components and defects the models can detect and classify.

Where this goes

Grid inspection is embodied AI in the air. A model-assisted loop, where the model produces signal and experts review it, is what scales detection across a continent of infrastructure.