PCB CT image element segmentation model based on boundary-attention-guided finetuning.

Abstract

Background: Computed Tomography (CT) technology is commonly used to realize non-destructive testing of Printed Circuit Board (PCB), and element segmentation is the key link in the process. Although the pretraining and finetuning paradigm alleviates the problem of labeling, PCB CT images are easily affected by uneven grayscale and layer penetration. This leads to difficult segmentation of boundaries and affect semantic understanding, resulting in jagged boundaries and even missing elements.

Objective: This paper aims to solve the problem of poor boundary segmentation in PCB CT image element segmentation.

Methods: To this end, we propose PCB CT image element segmentation model based on boundary-attention-guided finetuning. An improved boundary detection algorithm is proposed to enhance boundary sensing ability. In order to achieve non-fixed weight feature fusion, the Attention Feature Fusion module is designed to help boundary features better assist segmentation through attention mechanism.

Results: Experiments show that BAG-FTseg can achieve 89.5% mIoU on our PCB CT dataset, exceeding the baseline model by 0.9%, and the boundary-mIoU reaches 69.5%, 5.3% higher than the baseline model.

Conclusion: This method improves the accuracy of boundary segmentation of PCB elements and the efficiency of feature fusion through attention mechanism, which has practical significance.