Abstract. The inspection of solder joints on printed circuit boards is a difficult task because defects inside the joints cannot be observed directly. In addition, because anomalous samples are rarely obtained in a general anomaly detection situation, many methods use only normal samples in the learning phase. However, sometimes a small number of anomalous samples are available for learning. We propose a method to improve performance using a small number of anomalous samples for training in such situations. Specifically, our proposal is an anomaly detection method using an adversarial autoencoder (AAE) and Hotelling’s T-squared distribution. First, the AAE learns features of the solder joint following the standard Gaussian distribution from a large number of normal samples and a small number of anomalous samples. Then, the anomaly score of a solder joint is calculated by Hotelling’s T-squared method from the features learned by the AAE. Finally, anomaly detection is performed by thresholding using this anomaly score. In experiments, we show that our method performs anomaly detection with few false positives in such situations. Moreover, we confirmed that our method outperforms the conventional method using handcrafted features and a one-class support vector machine.

中文翻译：

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用于检测印刷电路板上焊接点异常的对抗性自动编码器

摘要。印刷电路板上焊点的检查是一项艰巨的任务，因为无法直接观察到焊点内部的缺陷。另外，由于在一般的异常检测情况下很少会得到异常样本，所以很多方法在学习阶段只使用正常样本。但是，有时有少量异常样本可供学习。我们提出了一种在这种情况下使用少量异常样本进行训练来提高性能的方法。具体来说，我们的提议是一种使用对抗性自动编码器 (AAE) 和 Hotelling 的 T 平方分布的异常检测方法。首先，AAE 从大量正常样本和少量异常样本中学习遵循标准高斯分布的焊点特征。然后，焊点的异常分数是根据 AAE 学习到的特征，通过 Hotelling 的 T 平方方法计算得出的。最后，通过使用该异常分数进行阈值化来执行异常检测。在实验中，我们表明我们的方法在这种情况下执行异常检测时几乎没有误报。此外，我们确认我们的方法优于使用手工特征和一类支持向量机的传统方法。