AttentDifferNet

This repository presents AttentDifferNet, a method introduced in "Attention Modules Improve Image-Level Anomaly Detection for Industrial Inspection: A DifferNet Case Study" WACV2024 CVF | arXiv.

Dataset

InsPLAD is the main dataset of this work.

Abstract

Within (semi-)automated visual industrial inspection, learning-based approaches for assessing visual defects, including deep neural networks, enable the processing of otherwise small defect patterns in pixel size on high-resolution imagery. The emergence of these often rarely occurring defect patterns explains the general need for labeled data corpora. To alleviate this issue and advance the current state of the art in unsupervised visual inspection, this work proposes a DifferNet-based solution enhanced with attention modules: AttentDifferNet. It improves image-level detection and classification capabilities on three visual anomaly detection datasets for industrial inspection: InsPLAD-fault, MVTec AD, and Semiconductor Wafer. In comparison to the state of the art, AttentDifferNet achieves improved results, which are, in turn, highlighted throughout our quali-quantitative study. Our quantitative evaluation shows an average improvement – compared to DifferNet – of 1.77 ± 0.25 percentage points in overall AUROC considering all three datasets, reaching SOTA results in InsPLAD-fault, an industrial inspection in-the-wild dataset. As our variants to AttentDifferNet show great prospects in the context of currently investigated approaches, a baseline is formulated, emphasizing the importance of attention for industrial anomaly detection both in the wild and in controlled environments.

Method

AttentDifferNet uses attention modules in its backbone to help it focus on the object to be inspected and ignore irrelevant information (e.g., the background). We show that coupling attention modules, such as SENets and CBAMs, to Unsupervised Anomaly Detection (UAD) methods, can improve their image-level performance in different contexts (in the wild or in controlled scenarios), both qualitatively and quantitatively. Attention modules coupling process differs for each UAD method, as shown in the paper.

Citing

Please cite our work if it was useful to your research:

@InProceedings{Vieira_2024_WACV,
    author    = {e Silva, Andr\'e Luiz Vieira and Sim\~oes, Francisco and Kowerko, Danny and Schlosser, Tobias and Battisti, Felipe and Teichrieb, Veronica},
    title     = {Attention Modules Improve Image-Level Anomaly Detection for Industrial Inspection: A DifferNet Case Study},
    booktitle = {Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)},
    month     = {January},
    year      = {2024},
    pages     = {8246-8255}
}

@article{doi:10.1080/01431161.2023.2283900,
   author    = {André Luiz Buarque Vieira e Silva, Heitor de Castro Felix, Franscisco Paulo Magalhães Simões, Veronica Teichrieb, Michel dos Santos, Hemir Santiago, Virginia Sgotti and Henrique Lott Neto},
   title     = {InsPLAD: A Dataset and Benchmark for Power Line Asset Inspection in UAV Images},
   journal   = {International Journal of Remote Sensing},
   volume    = {44},
   number    = {23},
   pages     = {1-27},
   year      = {2023},
   publisher = {Taylor & Francis},
   doi       = {10.1080/01431161.2023.2283900},
   URL       = {https://doi.org/10.1080/01431161.2023.2283900},
   eprint    = {https://doi.org/10.1080/01431161.2023.2283900},
}

Funding

DAAD – German Academic Exchange Service

CAPES – Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (Brazil)