  This study uses domain randomization to generate a synthetic RGB-D dataset
for training multimodal instance segmentation models, aiming to achieve
colour-agnostic hand localization in cluttered industrial environments. Domain
randomization is a simple technique for addressing the "reality gap" by
randomly rendering unrealistic features in a simulation scene to force the
neural network to learn essential domain features. We provide a new synthetic
dataset for various hand detection applications in industrial environments, as
well as ready-to-use pretrained instance segmentation models. To achieve robust
results in a complex unstructured environment, we use multimodal input that
includes both colour and depth information, which we hypothesize helps to
improve the accuracy of the model prediction. In order to test this assumption,
we analyze the influence of each modality and their synergy. The evaluated
models were trained solely on our synthetic dataset; yet we show that our
approach enables the models to outperform corresponding models trained on
existing state-of-the-art datasets in terms of Average Precision and
Probability-based Detection Quality.
