Optimizing the segment anything model for PCB component segmentation in x-ray images through few-shot parameter-efficient fine-tuning

Abstract

Segmentation of printed circuit board (PCB) components from X-ray images holds paramount significance as it constitutes a crucial step in design extraction and reverse engineering processes. Conventional pretrained deep learning segmentation models demand considerable resources and produce less-than-optimal outcomes and often results in overfitting due to the scarcity of the labeled PCB X-ray data. The Segment Anything Model (SAM), known for its versatility in semantic segmentation tasks, showcases its capability to effectively segment a wide array of objects found in natural images. Nonetheless, it encounters challenges when it comes to the complex design of PCB X-ray images, causing difficulty in accurately segmenting the components present in the circuit boards design. Adapting this foundation model to the unique challenges posed by PCB X-ray images, such as intricate component structures and variations in X-ray artifacts, requires careful modification and optimization. In this study, we propose a customized approach for segmenting components from X-ray images of PCBs that use a modified SAM model with parameter-efficient fine-tuning and few-shot generalization strategies. We introduce modifications to enhance the model's ability to capture intricate spatial relationships and effectively segment individual components. Our methodology focuses on the efficient adaptation of the foundation model to the unique characteristics of PCB X-ray images, including complex component structures and varying noise conditions. Leveraging few-shot learning techniques, we address the challenge of limited annotated data in the PCB X-ray domain, towards the aim of enabling the model to generalize effectively with minimal fine-tuning. Our work has the potential to pave the way for a novel solution to the challenge of implementing deep learning in a limited dataset by leveraging the capabilities of a foundation model.