Improving the Efficiency of Autoencoders for Visual Defect Detection with Orientation Normalization

Autoencoders (AE) can have an important role in visual inspection since they are capable of unsupervised learning of normal visual appearance and detection of visual defects as anomalies. Reducing the variability of incoming structures can result in more efficient representation in latent space and better reconstruction quality for defect free inputs. In our paper we investigate the utilization of spatial transformer networks (STN) to improve the efficiency of AEs in reconstruction and defect detection. We found that the simultaneous training of the convolutional layers of the AEs and the weights of STNs doesn't result in satisfactory reconstructions by the decoder. Instead, the STN can be trained to normalize the orientation of the input images. We evaluate the performance of the proposed mechanism, on three classes of input patterns, by reconstruction error and standard anomaly detection metrics.