  This paper describes a novel method of training a semantic segmentation model
for environment recognition of agricultural mobile robots by unsupervised
domain adaptation exploiting publicly available datasets of outdoor scenes that
are different from our target environments i.e., greenhouses. In conventional
semantic segmentation methods, the labels are given by manual annotation, which
is a tedious and time-consuming task. A method to work around the necessity of
the manual annotation is unsupervised domain adaptation (UDA) that transfer
knowledge from labeled source datasets to unlabeled target datasets. Most of
the UDA methods of semantic segmentation are validated by tasks of adaptation
from non-photorealistic synthetic images of urban scenes to real ones. However,
the effectiveness of the methods is not well studied in the case of adaptation
to other types of environments, such as greenhouses. In addition, it is not
always possible to prepare appropriate source datasets for such environments.
In this paper, we adopt an existing training method of UDA to a task of
training a model for greenhouse images. We propose to use multiple publicly
available datasets of outdoor images as source datasets, and also propose a
simple yet effective method of generating pseudo-labels by transferring
knowledge from the source datasets that have different appearance and a label
set from the target datasets. We demonstrate in experiments that by combining
our proposed method of pseudo-label generation with the existing training
method, the performance was improved by up to 14.3% of mIoU compared to the
best score of the single-source training.
