Success Case | Redeia: Identification of Damages in Power Lines Inspection with Synthetic Data Generation
Innovative and sustainable multinational energy group present in the electricity and gas business, with an annual turnover of 22 billion euros.
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The Challenge
The client aimed to automate power line inspections using aerial captured images to identify damage types. Leveraging advanced visual computing, the challenge was to enhance efficiency and accuracy in damage detection across the distribution network.
The Solution
Keepler developed an AI-driven solution on AWS employing Generative Adversarial Neural Networks (GAN) for damage identification and classification on drone images. By integrating Computer Vision models, six damage types were precisely identified, and a semantic segmentation model highlighted oxide regions on insulators and towers. This innovative approach leveraged synthetic data to improve image analysis and reduce model bias.
The Result
Keepler is a full-stack analytics services company specialized in the design, construction, deployment and operation of advanced public cloud analytics custom-made solutions. We bring to the market the Data Product concept, which is a fully automated, public cloud services-based, tailored software that adds advanced analytics, data engineering, massive data processing, and monitoring features. In addition, we help our customers transition to using public cloud services securely and improve data governance to make the organization more data-centric.
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