PCB-MC：印刷电路板中的缺失元件分析
PCB-MC: Missing Component Analysis in Printed Circuit Boards
- University of Twente（特文特大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本文提出PCB-MC数据集，用于印刷电路板缺失元件检测，通过板类型感知交叉验证评估监督与无监督方法，发现现有方法在多样布局上仍面临挑战。
AI中文摘要：
检测印刷电路板（PCB）上的缺失元件与传统的目标检测有根本不同，因为模型必须定位不存在的元件。我们引入了PCB-MC，一个基于RF100数据集构建的、带有足迹级标注的缺失元件检测精选数据集。该数据集包含197种不同的板类型，每种对应一个独特的PCB设计，每种类型有多个增强样本。我们还通过在PCB-MC上评估一系列多样化的监督和无监督方法，提供了基准结果。为确保公平评估，我们提出了板类型感知的交叉验证划分，以防止训练集和测试集之间的布局泄漏。监督模型在未见过的板设计上表现出较高的假阴性率，而无监督异常检测方法由于缺乏与板特定参考的空间对齐而完全失败。这些结果证实，在不同PCB布局上的缺失元件检测仍然是一个开放的挑战。我们发布了PCB-MC和所有训练协议，以支持工业检测中结构缺失检测的可复现研究。
英文摘要：
Detecting missing components on printed circuit boards (PCBs) differs fundamentally from conventional object detection, as the model must localize components that are not present. We introduce PCB-MC, a curated dataset for missing component detection with footprint level annotations built on top of the RF100 dataset. The dataset contains 197 distinct board types, each corresponding to a unique PCB design, with multiple augmented samples per type. We also provide benchmark results on PCB-MC by evaluating a diverse set of supervised and unsupervised methods. To ensure fair evaluation, we propose board type aware cross validation splits that prevent layout leakage between training and test sets. Supervised models showcase high false negative rates on unseen board designs, and unsupervised anomaly detection methods fail entirely due to the lack of spatial alignment with a board specific reference. These results confirm that missing component detection on diverse PCB layouts remains an open challenge. We release PCB-MC and all training protocols to support reproducible research on structural absence detection in industrial inspection.
