Element segmentation is a key step in nondestructive testing of Printed Circuit Boards (PCB) based on Computed Tomography (CT) technology. In recent years, the rapid development of self-supervised pretraining technology can obtain general image features without labeled samples, and then use a small amount of labeled samples to solve downstream tasks, which has a good potential in PCB element segmentation. At present, Masked Image Modeling (MIM) pretraining model has been initially applied in PCB CT image element segmentation. However, due to the small and regular size of PCB elements such as vias, wires, and pads, the global visual field has redundancy for a single element reconstruction, which may damage the performance of the model. Based on this issue, we propose an efficient pretraining model based on multi-scale local visual field feature reconstruction for PCB CT image element segmentation (EMLR-seg). In this model, the teacher-guided MIM pretraining model is introduced into PCB CT image element segmentation for the first time, and a multi-scale local visual field extraction (MVE) module is proposed to reduce redundancy by focusing on local visual fields. At the same time, a simple 4-Transformer-blocks decoder is used. Experiments show that EMLR-seg can achieve 88.6% mIoU on the PCB CT image dataset we proposed, which exceeds 1.2% by the baseline model, and the training time is reduced by 29.6 hours, a reduction of 17.4% under the same experimental condition, which reflects the advantage of EMLR-seg in terms of performance and efficiency.

翻译：元件分割是基于计算机断层扫描（CT）技术的印刷电路板（PCB）无损检测中的关键步骤。近年来，自监督预训练技术的快速发展能够在无标注样本的情况下获取通用图像特征，进而利用少量标注样本解决下游任务，在PCB元件分割中展现出良好潜力。目前，掩码图像建模（MIM）预训练模型已在PCB CT图像元件分割中得到初步应用。然而，由于PCB元件（如过孔、导线和焊盘）尺寸较小且形状规则，全局视场对单个元件重构存在冗余性，可能损害模型性能。针对此问题，我们提出了一种基于多尺度局部视场特征重构的高效预训练模型（EMLR-seg），用于PCB CT图像元件分割。在该模型中，首次将教师引导的MIM预训练模型引入PCB CT图像元件分割，并提出了多尺度局部视场提取（MVE）模块，通过聚焦局部视场来减少冗余性。同时，采用包含4个Transformer块的简洁解码器。实验表明，在我们提出的PCB CT图像数据集上，EMLR-seg可实现88.6%的mIoU，比基线模型提升1.2%，且训练时间减少29.6小时，在相同实验条件下降低17.4%，体现了EMLR-seg在性能和效率方面的优势。