[TAKEN] Detecting anomalies on electrical components using few-shot learning

Department: HMS Labs

Size: 1-4 students

Scope: Computer vison, Artificial Intelligence

Description: There is an increasing interest in intelligent applications for industrial use cases. One area where smart AI-driven applications are of particular interest is visual inspection of products along the production line. Traditional computer-vision-based approaches rely on large amounts of data to make accurate predictions. Acquiring enough data to achieve a satisfying level of accuracy is often challenging. The aim of this thesis is to develop a tool for quality inspection based on few-shot learning. Few-shot learning refers to the practice of feeding a learning model with a small amount of training data. The goal is to detect anomalies in mounted components on circuit boards. The system should be able to differentiate between defective and functioning boards.

The project and scope can be adjusted to the size of the group and the participants.

Prerequisites: Bachelor or master program within computer science or similar. Experience in a programming environment suitable for AI development like Python or Matlab, knowledge in artificial intelligence and computer vision, and expertise in C/C++, make, CMake is an advantage.

Contact person
Felix Nilsson
+46 7667 753 51
fenil@hms.se