We investigated the anomaly detection behaviour of three convolutional autoencoder types - a "standard” convolutional autoencoder (CAE), a variational convolutional autoencoder (VAE) and an adversarial convolutional autoencoder (AAE) - by applying them to different visual anomaly detection scenarios. First, we utilized our three autoencoder types to detect anomalous regions in two synthetically generated datasets. To investigate the convolutional autoencoders’ defect detection performances “in the industrial wild”, we applied the models on quality inspection images of non-defective and defective material regions. We compared the performances of all three autoencoder types based on their ability to detect anomalies and captured the training complexity by measuring the time needed for training them. Although the CAE is the simplest model, the trained model performed nearly as well as the more sophisticated autoencoder types, which depend on more complex training processes. For data that lacks regularity or shows purely stochastic patterns, all our autoencoders failed to compute meaningful results.
@inproceedings{schneideretal22visapp,
title={Analysis of the Future Potential of Autoencoders in Industrial Defect Detection},
author={Sarah Schneider and Doris Antensteiner and Daniel Soukup and Matthias Scheutz},
year={2022},
booktitle={16th International Conference on Computer Vision Theory and Applications},
volume={4},
pages={282--289}
url={https://hrilab.tufts.edu/publications/schneideretal22visapp.pdf}
doi={10.5220/0010777000003124}
}