FARAD2SORT: Fast Deep Learning and Deployment for Products Sorting (R-13343)

Deep learning is an artificial intelligence technology that has shown great potential in a large variety of applications. When applied to image analysis, it is an essential enabler for automation of repetitive but complex quality inspection tasks, development of robots that can recognize and manipulate objects, etc. So far, the use of this powerful technology requires deep technical knowledge of training strategies and a large amount of hand-annotated images, i.e. a long and costly development process. Currently the deployment of this technology delivers inconsistent results of variable performance, and it is difficult to debug and maintain. The goal of the FARAD2SORT project is to realize a technological framework to help engineers that have a general understanding of deep learning technology, but are not experts in it, to design, develop, and deploy deep learning vision based on 2D images for applications that require industrial object detection, object recognition and surface defects / anomaly detection & classification. The FARAD2SORT results will build on existing open-source deep learning software by adding tools that will make implementation easier, cheaper and more accurate and robust.

Keywords:Artificial Intelligence, Computer Vision, Deep learning, Open Source, Product Inspection and Sorting, Synthetic Data

Disciplines:Computer-aided design

Project type:Collaboration project