The Industry 4.0 paradigm has made machine vision systems a necessity in modern industrial facilities. They enable digitalisation of perception and provide valuable visual information about the production processes that facilitate intelligent information extraction and decision making. The main functional objective of the proposed project is to change the development and deployment pipeline of machine vision systems. Our goal is to shift the predominant paradigm of developing hand-engineered specific solutions into the direction of data-driven learning-based design and development that would enable more general, efficient, flexible and economical development, deployment and maintenance of machine vision systems. To this end, the main applied objective of the project is to develop a software framework that would enable such kind of development with as little and undemanding involvement of a human operator as possible, minimising the requirement for manual data acquisition and annotation by highly automating the entire process of data preparation and model training. This goal requires solving a number of scientific problems, such as developing core deep-learning methods and procedures for iterative, active, robust, few-shot, weakly-, self-, and un-supervised learning of visual models that will reach the required performance with minimal number of (manually annotated) training images.

The work programme will be divided into six work packages. Research will be conducted in the first four work packages that will address the following objectives of the project:

●Development of advanced data synthesis and augmentation methods for enabling supervised learning with unlimited annotated data (WP1).

●Development of novel efficient few-shot, active and robust methods for learning from limited amount of data (WP2).

●Development of unsupervised learning methods for visual appearance modelling without labelled data (WP3).

●Application of the developed methods to two use cases: 6DOF object pose detection and surface defect detection (WP4).

The remaining two work packages relate to the dissemination and exploitation of the results (WP5) and project management (WP6).

Project phases:

Year 1: Activities on work packages WP1, WP2, WP3, WP4, WP5, WP6

Year 2: Activities on work packages WP1, WP2, WP3, WP4, WP5, WP6

Year 3: Activities on work packages WP2, WP3, WP4, WP5, WP6