BugNIST a Large Volumetric Dataset for Object Detection Under Domain Shift

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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.
Original languageEnglish
Title of host publicationProceedings of the 18th European Conference on Computer Vision – ECCV 2024
Volume15090
PublisherSpringer
Publication date2025
Pages18-36
ISBN (Print)978-3-031-73410-6
ISBN (Electronic)978-3-031-73411-3
DOIs
Publication statusPublished - 2025
EventThe 18th European Conference on Computer Vision - Milano, Italy
Duration: 29 Sept 20244 Oct 2024

Conference

ConferenceThe 18th European Conference on Computer Vision
Country/TerritoryItaly
CityMilano
Period29/09/202404/10/2024

Keywords

  • Benchmark
  • Domain Shift
  • Volumetric Dataset
  • Volumetric Object Detection

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