Deep learning neural networks company Neurala has today announced a partnership with Laserpas, one of the fastest growing utility asset management companies in the EU.
Neurala’s computer vision and deep learning technology will be deployed as a critical element of Laserpas’ workflow, enabling more efficient aerial inspections and reducing the time it takes the company to analyze the data collected. Neurala’s technology will detect core components such as lattices, poles, and insulators, and identify damage or rust on those assets. This will allow Laserpas to eliminate safety risks for boots-on-the-ground inspectors, avoid hiring human labor to do dull tasks, and future-proof its business as it continues to scale.
“We are thrilled to be working with Laserpas as we establish a strong presence in the EU and continue to demonstrate the value of AI in aerial-based inspections, drastically improving efficiency in a field that needs it,” said Massimiliano Versace, co-founder and CEO of Neurala. “Laserpas has come to Neurala to create a complete deployable solution, and our partnership will illustrate the value of working with a company like Neurala that can do it all.”
“Automation is a key focus for Laserpas as we continue scaling our business and maintain a tactical advantage over the competition,” said Mantas Vaskela, co-founder and CEO of Laserpas. “Today, we are collecting pictures and videos containing critical infrastructure components that need to be carefully analyzed. AI assisted data analysis is the only way forward for us, and a cost-benefit examination of alternative solutions pointed us to Neurala as the most efficient and cost-effective solution for our long-term business targets. Neurala is the right partner to deploy advanced AI that can improve in performance as we gather more and more data from the field.”
Laserpas will be using Neurala’s new Brain Builder tool, the first cost-effective, centralized AI workflow management tool that enables fast and accurate creation of large quantities of data for DNN training. Brain Builder has purpose-built annotation tools to simplify the process and drastically reduce tagging time in both static pictures and videos, leveraging on-the-fly learning to increase DNN precision as more data is added. Users can download the dataset to create a deployable DNN using industry-standard tools like TensorFlow and Caffe or export the data in different formats to train multiple types of neural networks.