Exploring the Effect of Annotation Quality on PCB Component Segmentation

Abstract

Abstract Due to the continuous outsourcing of printed circuit board (PCB) fabrication, PCB counterfeits and Trojans have increased by a significant margin, and this has necessitated rapid and advanced hardware assurance techniques. PCB Image segmentation is the primary step in PCB assurance. Over the years, few PCB component segmentation methods have been proposed and none of those have provided a definite benchmark of performance. Besides those methods haven’t discussed how the performance is correlated with underlying data or annotation quality. In this work, we present a benchmark on PCB image segmentation along with a high-quality dataset. In addition, we explore how annotation quality affects component segmentation and present possible future research directions to work with coarse annotations to alleviate the human effort behind full data annotation tasks. We have analyzed the performance of the preferred Deep Neural Network (DNN) architecture with the data annotation quality and presented the direction to leverage the outcome with limited quality annotations. Finally, we present the qualitative as well as the quantitative results to demonstrate the performance of our techniques and provide observations and future research directions on the overall task.