We introduce the Densely Segmented Supermarket (D2S) dataset, a novel
benchmark for instance-aware semantic segmentation in an industrial domain. It
contains 21,000 high-resolution images with pixel-wise labels of all object
instances. The objects comprise groceries and everyday products from 60
categories. The benchmark is designed such that it resembles the real-world
setting of an automatic checkout, inventory, or warehouse system. The training
images only contain objects of a single class on a homogeneous background,
while the validation and test sets are much more complex and diverse. To
further benchmark the robustness of instance segmentation methods, the scenes
are acquired with different lightings, rotations, and backgrounds. We ensure
that there are no ambiguities in the labels and that every instance is labeled
comprehensively. The annotations are pixel-precise and allow using crops of
single instances for articial data augmentation. The dataset covers several
challenges highly relevant in the field, such as a limited amount of training
data and a high diversity in the test and validation sets. The evaluation of
state-of-the-art object detection and instance segmentation methods on D2S
reveals significant room for improvement.

本文介绍了 Densely Segmented Supermarket（D2S）数据集，该数据集是一个新颖的实例感知语义分割基准，包含 21000 张高分辨率图像，具有所有对象实例的逐像素标签，其对象包括 60 个类别的杂货和日常用品。我们确保标签中没有歧义，每个实例都有全面的标注。该数据集涵盖了许多高度相关的挑战，如有限的训练数据和测试集，验证集的高多样性等。对 D2S 上最先进的物体检测和实例分割方法进行评估，结果表明还有很大的提升空间。