Exploring the Capability of Kernel- and Correlation-Based Learning on PCB Component Segmentation

Due to the continuous increase in the globalized outsourcing of printed circuit board (PCB) fabrication, PCB counterfeits have increased by a significant margin, necessitating 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 has provided a definite performance benchmark. Besides, those methods have not discussed how the performance is correlated with underlying data or annotation quality. This work presents a PCB image segmentation benchmark. 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 and Transformer 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.