High-resolution hyperspectral images are set to take quality assurance and increased efficiency in industry, agriculture, and autonomous driving to a new level. The Fraunhofer Institute for Material and Beam Technology IWS in Dresden heads a European consortium of twelve companies and research institutes tackling this objective in the project “HyperImage – a universal spectral image sensor platform for industry, agriculture, and autonomous driving”. The European Union is funding the project with a total of 5.6 million euros over a period of three and a half years.

Under the leadership of Fraunhofer IWS, a European consortium of industry and science is developing a modular, cloud-based platform for the semi-automated evaluation of spectrally high-resolution images. “With innovative photonic components and the use of AI algorithms, we strive to translate image data into relevant functional product properties,” explains Alexander Kabardiadi-Virkovski, who acts as head of the HyperImage project at the IWS. “This will enable us to classify products and make decisions more easily and quickly, as well as monitor processes better.” The research partners also want to develop and test algorithms to harmonize and standardize hyperspectral data from European camera and cloud infrastructure manufacturers. This standard should unify the variety of existing image formats and enable the transferability of image information between different camera manufacturers.

The project results will be used for quality control in high-performance electronics production, monitoring automated vertical plant cultivation, integrating spectral image-based vision and navigation functions in autonomous vehicles, and developing a high-resolution hyperspectral vision system for unmanned geosurveillance drones. These use cases serve to position HyperImage as a universal solution for object recognition, detailed product and material analysis, and reliable quality control in various industries. The automated platform will increase yields and reduce production costs in vertical farming, save fuel, increase operating speeds for autonomous off-road driving, reduce weight, and increase flight time for drones.