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BugNIST a Large Volumetric Dataset for Object Detection Under Domain Shift

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Computer Vision – ECCV 2024 (ECCV 2024)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 15090))

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Abstract

Domain shift significantly influences the performance of deep learning algorithms, particularly for object detection within volumetric 3D images. Annotated training data is essential for deep learning-based object detection. However, annotating densely packed objects is time-consuming and costly. Instead, we suggest training models on individually scanned objects, causing a domain shift between training and detection data. To address this challenge, we introduce the BugNIST dataset, comprising 9154 micro-CT volumes of 12 bug types and 388 volumes of tightly packed bug mixtures. This dataset is characterized by having objects with the same appearance in the source and target domains, which is uncommon for other benchmark datasets for domain shift. During training, individual bug volumes labeled by class are utilized, while testing employs mixtures with center point annotations and bug type labels. Together with the dataset, we provide a baseline detection analysis, with the aim of advancing the field of 3D object detection methods.

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Notes

  1. 1.

    https://abdahl.github.io/bugnist/.

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Acknowledgments

We acknowledge the 3D Imaging Center at DTU, the Infrastructure for Quantitative AI-based Tomography (QUAITOM), supported by the Novo Nordisk Foundation (grant number NNF21OC0069766) and STUDIOS: Segmenting Tomograms Using Different Interpretation of Simplicity funded by the Villum Foundation (grant number VIL50425).

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Authors and Affiliations

  1. Technical University of Denmark, Kgs. Lyngby, Denmark

    Patrick Møller Jensen, Vedrana Andersen Dahl, Rebecca Engberg, Carsten Gundlach, Hans Marin Kjer & Anders Bjorholm Dahl

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  1. Patrick Møller Jensen
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Correspondence to Anders Bjorholm Dahl .

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Editors and Affiliations

  1. University of Birmingham, Birmingham, UK

    Aleš Leonardis

  2. University of Trento, Trento, Italy

    Elisa Ricci

  3. Technical University of Darmstadt, Darmstadt, Germany

    Stefan Roth

  4. Princeton University, Princeton, NJ, USA

    Olga Russakovsky

  5. Czech Technical University in Prague, Prague, Czech Republic

    Torsten Sattler

  6. École des Ponts ParisTech, Marne-la-Vallée, France

    Gül Varol

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Jensen, P.M., Dahl, V.A., Engberg, R., Gundlach, C., Kjer, H.M., Dahl, A.B. (2025). BugNIST a Large Volumetric Dataset for Object Detection Under Domain Shift. In: Leonardis, A., Ricci, E., Roth, S., Russakovsky, O., Sattler, T., Varol, G. (eds) Computer Vision – ECCV 2024. ECCV 2024. Lecture Notes in Computer Science, vol 15090. Springer, Cham. https://doi.org/10.1007/978-3-031-73411-3_2

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