Applied Vision

Intelligent Augmentation Methods for Training Defect Detection on Circuit Boards

Authors: Olaf Kaehler (Joanneum Research) , Werner Bailer (Joanneum Research) , Georg Thallinger (Joanneum Research)

Abstract

We discuss intelligent data augmentation strategies to help training object detection models from low-volume datasets. In particular, many industrial inspection tasks suffer from a lack of samples showing defects in the training data, and furthermore the failure cases are typically heterogeneous, leaving only a handful of samples for each of them. For our application scenario of printed circuit board (PCB) inspection, we propose and evaluate a strategy for synthesizing defects as well as a strategy to copy-paste difficult, challenging, or otherwise rare cases into the training images. Maintaining this library of challenging or rare cases offers an easy way to update the model and integrate feedback after deployment. We evaluate the benefits of the augmentation strategies in experiments and present a reliable and accurate PCB inspection model trained with only 25 images.

Keywords:

How to Cite: Kaehler, O. , Bailer, W. & Thallinger, G. (2026) “Intelligent Augmentation Methods for Training Defect Detection on Circuit Boards”, Proceedings of the Austrian Symposium on AI, Robotics, and Vision. 3(1).