Semi-supervised anomaly detection for visual quality inspection

In this paper a semi-supervised method for the detection of anomalies in both texture- and object-based product images is presented. The method exploits a pre-trained Convolutional Neural Network (CNN) autoencoder that is blended with a statistical-based transformation of the neural network embedding layer in order to remove anomalies from the input image. The “cleaned” version of the input image is then compared with the input image itself in order to spatially localize the anomalies. The method does not require a specific training of the CNN to be applied to a new class of product, but it requires a very fast domain adaptation based on only “anomaly-free” examples. Experiments conducted on a publicly available dataset made of fifteen texture- and object-based classes show that overall performance is better than the state of the art of about 4%. In the case of texture-based classes the proposed method outperforms the state of the art of about 13%. In the case of object-based classes, the proposed method reaches overall the same performance of the state of the art. In this case, apart from 3 cases, that is “bottle”, “transistor” and “metal nut”, the proposed method performs better than the state of the art in 7 object classes out of 10.