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 domain, 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, aiming at advancing the field of 3D object detection methods.

翻译：域迁移显著影响深度学习算法的性能，尤其是在三维体素图像中的目标检测任务中。基于深度学习的目标检测需要带注释的训练数据。然而，对密集堆叠的物体进行标注既耗时又昂贵。为此，我们建议使用独立扫描的单个物体进行模型训练，这会导致训练数据与检测数据之间存在域迁移。为解决这一挑战，我们提出了BugNIST数据集，包含9154个12类昆虫的微CT体素数据以及388个密集堆叠昆虫混合物的体素数据。该数据集的独特之处在于：源域与目标域中物体的外观保持一致，这在其他域迁移基准数据集中较为罕见。训练阶段使用按类别标注的单个昆虫体素数据，测试阶段则采用带有中心点标注和昆虫类别标签的混合数据。我们还随数据集提供了基线检测分析，旨在推动三维目标检测方法领域的发展。