AGROVIS, “Intelligent Visual Computing for products/processes in the agri-food sector” is an industrial research project framed in the field of computer vision (digital enabler of Industry 4.0) associated with the agri-food sector.
The main objective is to obtain new artificial vision methodologies that can contribute to comprehensive hardware/software quality control solutions in the industry so present in Castilla y León.
- Explore technologies that open up the possibility of automatically inspecting the interior of products safely.
- Increase the potential of automatic classification of natural texture images to achieve reliable performance.
- Incorporate the ability to induce knowledge in machine learning systems, to learn with a single example.
- Identify, catalogue and characterize the products of the agri-food industry in Castilla y León.
- Analyze the non-ionizing spectral range that penetrates the matter.
- Enhance the classification of natural textures by combining complex classifiers
- Develop techniques for the generation of fictitious databases, from a single images.
Obtaining new artificial vision methodologies that can contribute to comprehensive hardware/software quality control solutions in the agri-food industry of Castilla y León.
R&D projects of regional interest aimed at excellence and competitive improvement of Technological Centers of Castilla y León-2020
Total Budget: 954.453,06 €
CARTIF Budget: 906.730,41 €
Duration: september 2020 – december 2022
Luis Miguel González
Industrial and Digital Systems Division
Industrial Solutions Projects:
The Intrusion-G4 project seeks new technologies to overcome the challenge of increasing the degree of security of the intrusion detection sensors currently available on the market.
The TRREX project (Extended Range Robot Enabling Technologies for the Flexible Factory) investigates and develops technologies that contribute to the deployment of mobile industrial robots for the factories of the future. These systems will increase the flexibility of the plants and allow the optimization of industrial processes, improving their productivity.
i-Visart, “New artificial vision methodologies for the visual inspection of highly reflective and textured surfaces”.
It is an industrial research project framed in the field of computer vision (digital enabler of industry 4.0) associated with the industrial sector.
El grupo de trabajo de Hábitat Eficiente del clúster AEICE persigue el objetivo de mejorar la competitividad de sus miembros, principalmente PYMEs, en temas de innovación en procesos, productos, modelos de negocio e internacionalización.
A new intelligent system has been developed for manual assembly positions in the industry. This system aims to minimize assembly errors by operators by validating operations, providing information and guiding the operator in real time through a man-machine interface based on Augmented Reality
Nowadays in hot and cold lamination of “long” elements only the detection of punctual defects are made, not longitudinal defects. Therefore, the technological leap and the differentiating key of future products is to detect both, punctual and longitudinal defects on the production of flat steel in the rolling process, either the cold or hot process.
MARCA provides the maintenance operator with tools that facilitate access to content, communications and technologies necessary for the guidance, support and registration of maintenance work. It is based on technologies of augmented reality, mobility and communication, as well as a knowledge base for incident management.
INCEPTION develops new methods and tools for automated 3D modelling and analysis of European cultural assets proposing advancements on hardware and software, as well as new approaches for Cultural Heritage 3D data inclusive access and exploitation by means of the so called INCEPTION-Platform.
The aim of MODINTECO project is to develop prototypes of Automatic Tool Changers for milling machines, modular and adaptable, intelligent and autonomous, universal..
The purpose of the CALYPSO project is that the inspection process can be enriched by automatic pattern recognition techniques.