Abstract
In this paper, we introduce a one-class learning approach for detecting modifications in assembled printed circuit boards (PCBs) based on photographs taken without tight control over perspective and illumination conditions. Anomaly detection and segmentation are essential for several applications, where collecting anomalous samples for supervised training is infeasible. Given the uncontrolled environment and the huge number of possible modifications, we address the problem as a case of anomaly detection, proposing an approach that is directed towards the characteristics of that scenario, while being well suited for other similar applications. We propose a loss function that can be used to train a deep convolutional autoencoder based only on images of the unmodified board—which allows overcoming the challenge of producing a representative set of samples containing anomalies for supervised learning. We also propose a function that explores higher-level features for comparing the input image and the reconstruction produced by the autoencoder, allowing the segmentation of structures and components that differ between them. Experiments performed on a dataset built to represent real-world situations (which we made publicly available) show that our approach outperforms other state-of-the-art approaches for anomaly segmentation in the considered scenario, while producing comparable results on a more general object anomaly detection task.