Authors - Pankaj Mishra, Ricardo Verk, Daniele Fornasier, Claudio Piciarelli, Gian Luca Foresti
Abstract- We present a transformer-based image anomaly detection and localization network. Our proposed model is a combination of a reconstruction-based approach and patch embedding. The use of transformer networks helps preserving the spatial information of the embedded patches, which is later processed by a Gaussian mixture density network to localize the anomalous areas. In addition, we also publish BTAD, a real-world industrial anomaly dataset. Our results are compared with other state-of-the-art algorithms using publicly available datasets like MNIST and MVTec.
The network is inspired from Vision Transformer. It adapts the trasnformer network
for image anomaly detection and localization.
Source: BeanTech srl License type: CC-BY-SA
Dataset contains RGB images of three industrial products – Scan to download
The images are captured from the industrial image acquisition systems and then cropped and log transformation was applied to respect privacy policy of the data owner(Beantech). Later pixel precise ground truth has been added by manually annotating the data using commercially available annotation tool "SuperAnnotate"
- Product 1 : Contains 400 images of 1600x1600 pixels
- Product 2 : Contains 1000 images of 600x600 pixels
- Product 3 : Contains 399 images of 800x600 pixels
- MVTec Dataset - Real world anomaly dataset. contains 5354 high-resolution color and grey images of different texture and object categories.
- BTAD Dataset - Consists of high resolution 1.8K RGB images of industrial products.
- Choice of number of Gaussian’s in the mixture model is justified with increasing number of Gaussian’s.
- PRO Score first increases and then becomes constant
- Gaussian noise has been added to the encoded features from the transformer for regularization.
- With Noise added the PRO score is 0.897 in contrary to 0.807 without noise.
python train.py -p "hazelnut"
If you use this dataset, please cite it using the following reference:
P. Mishra, R. Verk, D. Fornasier, C. Piciarelli, G.L. Foresti
"VT-ADL: A Vision Transformer Network for Image Anomaly Detection and Localization"
30th IEEE/IES International Symposium on Industrial Electronics (ISIE)
Kyoto, Japan, June 20-23, 2021
BibTeX:
@inproceedings{
mishra21-vt-adl,
author = {Mishra, Pankaj and Verk, Riccardo and Fornasier, Daniele and Piciarelli, Claudio and Foresti, Gian Luca},
title = {{VT-ADL}: A Vision Transformer Network for Image Anomaly Detection and Localization},
booktitle = {30th IEEE/IES International Symposium on Industrial Electronics (ISIE)},
year = {2021},
month = {June},
location = {Kyoto, Japan}
}