Ongoing
The project develops image-based anomaly detection methods for industrial quality control, with particular attention to scarce defective data, explainability, and distributed deployment. It combines synthetic defect generation, guided-attention architectures, and lightweight diffusion models to support anomaly classification, localization, and segmentation. Experimental results on MVTec AD show that generated defects improve recognition of real anomalies, while the proposed models provide competitive accuracy with reduced reliance on external datasets and large pretrained architectures.
Ongoing
The project develops computer-vision and deep-learning methods for perception in autonomous and assisted-driving systems. Its activities focus on monocular depth estimation, pedestrian and cyclist distance assessment, semantic segmentation, and explainable scene understanding using low-cost RGB cameras. The proposed approaches improve depth accuracy and visual interpretability while reducing reliance on expensive sensors such as LiDAR.
Ongoing
EdgeAI develops secure, scalable, energy-efficient, and trustworthy artificial-intelligence technologies for deployment across the edge continuum, from embedded devices to distributed edge infrastructures. The project integrates hardware/software co-design, model optimisation, explainability, and real-time processing in industrial demonstrators spanning digital industry, energy, agri-food, mobility, and digital society.
Ongoing
The project develops image-based methods for automatic granulometry, enabling contactless estimation of particle-size distributions for industrial and scientific applications. It combines neural processing, synthetic virtual environments, stereo vision, metric 3D reconstruction, and computational-intelligence techniques to estimate particle length, width, and thickness. The proposed systems reduce the limitations of manual sampling and conveyor-based inspection while achieving millimetric and sub-millimetric accuracy in realistic inline conditions.
Completed
I-PAN project is a 36 months project co-financed by the European 7th Framework Programme of Research and Develoment which aims at providing novel and highly environmental friendly solutions in the field of the engineered wood (EW) based boards. The woodworking industry is a very relevant sector in Europe and the engineered wood (EW) sector is substantially advanced both in terms of higher quality wooden materials and in terms of production and manufacturing and panels and plywood.
Ongoing
Time-to-market and high product quality standards are pushing the use of automatic visual inspection systems for defect detection in a wide broad of applications. The defect detection of particle boards requires the identification of all the printed and natural wood defects that can occur.
Completed
Real-time quality monitoring in laser cutting applications is a key issue in high-tech steel manufacturing industries.
Completed
Real-time monitoring of the laser-based applications is becoming a main issue for quality analysis in the steel manufacturing industry.
Ongoing
Development of adaptive deep-learning methods for detecting DDoS attacks and other network intrusions in heterogeneous IoT and Industrial IoT environments, with particular attention to transfer learning, federated learning, limited labelled data, privacy, and evolving threats.
Completed
The estimation of the volume occupied by an object is an important task in the fields of granulometry, quality control, and archaeology. An accurate and well know technique for the volume measurement is based on the Archimedes' principle. However, in many applications it is not possible to use this technique and faster contact-less techniques based on image processing or laser scanning should be adopted.
Ongoing
The classification of wood types is needed in many industrial sectors, since it can provide relevant information concerning the features and characteristics of the final product (appearance, cost, mechanical properties, etc.).
