Complete Thermal and RGB Image Enhancing Service (Super Resolution) for powerline drone inspections [I-NERGY]
The projects SuperPower and SuperPower 2.0 developed thanks to I-NERGY open calls has developed super-resolution algorithms to increment the pixels in thermal and RGB images using Convolutional Neural Networks.
The end-goal of the developed system is to be able to provide end clients (power line owners) with the best inspection possible of the infrastructure so they can detect defects as early as possible to perform maintenance before the defect becomes a real safety concern. This AI asset is one of the parts of the complete data value chain in the inspection process
of the powerlines.
This Software offers the opportunity to apply AI to captured thermal and RGB images with drones in order to enhance the quality of the image increasing the number of pixels.
The service aims to automatically increase the number of pixels in an image. The service follows this process:
-The client captures the visual and thermal data in .jpg format using drones.
-The client transfers the data from the drones to a computer where the data is uploaded into the cloud and sent to our team. Depending on the demand of the client different platforms to share the data can be used.
-Our team receives the data, and stores it in an input folder. After that, the team executes the super-resolution algorithms developed in the project to obtain the enhanced photos in an output folder.
-The output data is sent back to the client using the demanded service.
After the complete process, the client will have enhanced thermal and visual images, so the defects are easily detected during the subsequent defect revision phase.
With this service, we aim to provide image enhancement service to companies that inspect power lines using super-resolution. This technology enables us to process images to increase the number of pixels using AI. This way, the processing of the images by AI or humans can be performed by detecting smaller defects thus detecting such defects earlier. Consequently, with this technology, we aim to improve the safety of the grid enhancing the predictive maintenance processes.
This project has received funding from the European Union's Horizon 2020 research and innovation programme within the framework of the I-NERGY Project, funded under grant agreement No 101016508