Industrial and Medical Anomaly Detection Through Cycle-Consistent Adversarial Networks

In this study, a new Anomaly Detection (AD) approach for real-world images is proposed. This method leverages the theoretical strengths of unsupervised learning and the data availability of both normal and abnormal classes. The AD is often formulated as an unsupervised task motivated by the frequent imbalanced nature of the datasets, as well as the challenge of capturing the entirety of the abnormal class. Such methods only rely on normal images during training, which are devoted to be reconstructed through an autoencoder architecture for instance. However, the information contained in the abnormal data is also valuable for this reconstruction. Indeed, the model would be able to identify its weaknesses by better learning how to transform an abnormal (or normal) image into a normal (or abnormal) image. Each of these tasks could help the entire model to learn with higher precision than a single normal to normal reconstruction. To address this challenge, the proposed method utilizes Cycle-Generative Adversarial Networks (Cycle-GANs) for abnormal-to-normal translation. To the best of our knowledge, this is the first time that Cycle-GANs have been studied for this purpose. After an input image has been reconstructed by the normal generator, an anomaly score describes the differences between the input and reconstructed images. Based on a threshold set with a business quality constraint, the input image is then flagged as normal or not. The proposed method is evaluated on industrial and medical images, including cases with balanced datasets and others with as few as 30 abnormal images. The results demonstrate accurate performance and good generalization for all kinds of anomalies, specifically for texture-shaped images where the method reaches an average accuracy of 97.2% (85.4% with an additional zero false negative constraint).

Composite Score for Anomaly Detection in Imbalanced Real-World Industrial Dataset

In recent years, the industrial sector has evolved towards its fourth revolution. The quality control domain is particularly interested in advanced machine learning for computer vision anomaly detection. Nevertheless, several challenges have to be faced, including imbalanced datasets, the image complexity, and the zero-false-negative (ZFN) constraint to guarantee the high-quality requirement. This paper illustrates a use case for an industrial partner, where Printed Circuit Board Assembly (PCBA) images are first reconstructed with a Vector Quantized Generative Adversarial Network (VQGAN) trained on normal products. Then, several multi-level metrics are extracted on a few normal and abnormal images, highlighting anomalies through reconstruction differences. Finally, a classifer is trained to build a composite anomaly score thanks to the metrics extracted. This three-step approach is performed on the public MVTec-AD datasets and on the partner PCBA dataset, where it achieves a regular accuracy of 95.69% and 87.93% under the ZFN constraint.