Visual Quality Control in Manufacturing

Project description:

The project objective is to investigate the usage of Deep Neural Networks (DNNs) for on-the-edge image analytics, i.e., product control in the industrial domain. The ever-growing throughput and quality demands of modern manufacturing make it impossible to rely on the human eye for a rising number of quality assessment procedures. This development leads to the introduction of computer vision algorithms, widely used in different fields, e.g., food industry, production of printed board-circuits. This project will investigate ways to improve the explainability of DNN-driven classification. An additional target is to investigate methods enabling classifiers to adapt to varying production conditions. 

  • Starting date: 01/01/2020
  • Duration: 48 months
  • Funding source: DataThings  
  • Researchers: Joe Lorentz, Inder Pal Singh, Prof. Djamila Aouada (PI)
  • Partners: DataThings