In recent years, near-infrared hyperspectral imaging has been widely used in various fields of application: from neuroscience to robotics and from agriculture to pharmaceuticals. While RGB cameras are suitable for characterizing objects based on their color and shape, near-infrared hyperspectral technology may be used for more demanding applications allowing to identify the screened materials based on their chemical composition rather than only their visible properties. STAM has exploited this powerful tool in several R&D projects, mainly related to the recycling field. In particular, an automatic robotic cell has been developed for indexing and sorting of construction and demolition waste (CDW).
One of STAM ongoing project that exploits hyperspectral imaging is ATLANTES – quality control of carbon parts based on machine vision, that has been made possible thanks to the TRINITY Demonstration Program. This project aims at increasing the agility level of carbon-fibre-reinforced polymers (CFRP) part manufacturers thanks to the automation of the quality inspection process of their products. A machine vision method based on hyperspectral NIR and RGB data will be deployed, aimed at classifying faulty CFRP parts. Thanks to ATLANTES, it will be possible to reduce the number of false-positive, save material and cost, and reduce CO2 emissions.
ATLANTES is part of a sub-project that has indirectly received funding from the European Union’s H2020 research and innovation programme via an Open Call issued and executed under project TRINITY (grant agreement No 825196).
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