Open Access

ARTICLE

PCB CT Image Element Segmentation Model Optimizing the Semantic Perception of Connectivity Relationship

Chen Chen, Kai Qiao, Jie Yang, Jian Chen, Bin Yan^*

School of Information Systems Engineering, PLA Strategy Support Force Information Engineering University, Zhengzhou, 450001, China

* Corresponding Author: Bin Yan. Email:

Computers, Materials & Continua 2024, 81(2), 2629-2642. https://doi.org/10.32604/cmc.2024.056038

Received 12 July 2024; Accepted 29 September 2024; Issue published 18 November 2024

Abstract

Computed Tomography (CT) is a commonly used technology in Printed Circuit Boards (PCB) non-destructive testing, and element segmentation of CT images is a key subsequent step. With the development of deep learning, researchers began to exploit the “pre-training and fine-tuning” training process for multi-element segmentation, reducing the time spent on manual annotation. However, the existing element segmentation model only focuses on the overall accuracy at the pixel level, ignoring whether the element connectivity relationship can be correctly identified. To this end, this paper proposes a PCB CT image element segmentation model optimizing the semantic perception of connectivity relationship (OSPC-seg). The overall training process adopts a “pre-training and fine-tuning” training process. A loss function that optimizes the semantic perception of circuit connectivity relationship (OSPC Loss) is designed from the aspect of alleviating the class imbalance problem and improving the correct connectivity rate. Also, the correct connectivity rate index (CCR) is proposed to evaluate the model’s connectivity relationship recognition capabilities. Experiments show that mIoU and CCR of OSPC-seg on our datasets are 90.1% and 97.0%, improved by 1.5% and 1.6% respectively compared with the baseline model. From visualization results, it can be seen that the segmentation performance of connection positions is significantly improved, which also demonstrates the effectiveness of OSPC-seg.

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Keywords

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