For visual inspection and quality control

The overall objective of the ML4VI project is the identification, testing and tuning of an innovative approach, which bases the development of product quality control and visual inspection systems on the use of Artificial Intelligence and automatic training techniques (Machine Learning and Deep Learning), starting from acquired images and measurements of some physical characteristics of products, thus arriving at the realization of a suite of deep network architectures that can be easily re-trained and re-applied as the product type changes. The scope of ML4VI is therefore aimed at increasing the competitiveness of enterprises, particularly by improving their product quality control phase, and innovating their production processes. Testing and validation of the approach will take place by addressing Use Cases brought by partner companies, and validation of the results will be implemented with demonstration of a prototype system in an operational environment.

  • Provision of services, Pilot Line and infrastructure
    The project will make use of the technologies, infrastructure and services offered by BI-REX. In relation to the Pilot Line, BI-REX will provide equipment and facilities for proof of concept and use cases; the project will make use of the General Contractor service (aimed at recruiting Research Scholars through the Universities collaborating in the project).
  • Sharing, use, dissemination of produced material and know-how
    All content/materials produced will be used by BI-REX with a view to dissemination and diffusion of know-how. This will cover both training activities and the provision of support and technology transfer services towards companies. The results obtained may also be used by BI-REX to pursue further development and industrial research activities.
  • Marposs SpA (Coordinatore)
  • Philip Morris Manufacturing and Technology Bologna SpA
  • SACMI Imola SC
  • Alascom Srl

Call: Call 2

Area: Advanced systems for production process management

Sub Area: Visual Inspection / Selection for quality control

Implementation period: 19.12.2020 – 18.06.2022