A Triad of Approaches for PCB Component Segmentation and Classification using U-Net, SAM, and Detectron2

The segmentation and classification of Printed Circuit Board (PCB) components offer multifaceted applications- primarily design validation, assembly verification, quality control optimization, and enhanced recycling processes. However, this field of study presents numerous challenges, mainly stemming from the heterogeneity of PCB component morphology and dimensionality, variations in packaging methodologies for functionally equivalent components, and limitations in the availability of image data. 

This study proposes a triad of approaches consisting of two segmentation-based and a classification-based architecture for PCB component detection. The first segmentation approach introduces an enhanced U-Net architecture with a custom loss function for improved multi-scale classification and segmentation accuracy. The second segmentation method leverages transfer learning, utilizing the Segment Anything Model (SAM) developed by Meta’s FAIR lab for both segmentation and classification. Lastly, Detectron2 with a ResNeXt-101 backbone, enhanced by Feature Pyramid Network (FPN), Region Proposal Network (RPN), and Region of Interest (ROI) Align has been proposed for multi-scale detection. The proposed methods are implemented on the FPIC dataset to detect the most commonly appearing components (resistor, capacitor, integrated circuit, LED, and button) in PCB. The first method outperforms existing state-of-the-art networks without pre-training, achieving a DICE score of 94.05%, an IoU score of 91.17%, and an accuracy of 94.90%. On the other hand, the second one surpasses both the previous state-of-the-art network and U-net in segmentation, attaining a DICE score of 97.08%, an IoU score of 93.95%, and an accuracy of 96.34%. Finally, the third one, being the first transfer learning-based approach to perform individual component classification on PCBs, achieves an average precision of 89.88%. Thus, the proposed triad of approaches will play a promising role in enhancing the robustness and accuracy of PCB quality assurance techniques.