TITLE: RB-Dust -- A Reference-based Dataset for Vision-based Dust Removal 

ABSTRACT: Dust in the agricultural landscape is a significant challenge and influences,
for example, the environmental perception of autonomous agricultural machines.
Image enhancement algorithms can be used to reduce dust. However, these require
dusty and dust-free images of the same environment for validation. In fact, to
date, there is no dataset that we are aware of that addresses this issue.
Therefore, we present the agriscapes RB-Dust dataset, which is named after its
purpose of reference-based dust removal. It is not possible to take pictures
from the cabin during tillage, as this would cause shifts in the images.
Because of this, we built a setup from which it is possible to take images from
a stationary position close to the passing tractor. The test setup was based on
a half-sided gate through which the tractor could drive. The field tests were
carried out on a farm in Bavaria, Germany, during tillage. During the field
tests, other parameters such as soil moisture and wind speed were controlled,
as these significantly affect dust development. We validated our dataset with
contrast enhancement and image dehazing algorithms and analyzed the
generalizability from recordings from the moving tractor. Finally, we
demonstrate the application of dust removal based on a high-level vision task,
such as person classification. Our empirical study confirms the validity of
RB-Dust for vision-based dust removal in agriculture.
Dust in the agricultural landscape is a significant challenge and influences,
for example, the environmental perception of autonomous agricultural machines.
Image enhancement algorithms can be used to reduce dust. However, these require
dusty and dust-free images of the same environment for validation. In fact, to
date, there is no dataset that we are aware of that addresses this issue.
Therefore, we present the agriscapes RB-Dust dataset, which is named after its
purpose of reference-based dust removal. It is not possible to take pictures
from the cabin during tillage, as this would cause shifts in the images.
Because of this, we built a setup from which it is possible to take images from
a stationary position close to the passing tractor. The test setup was based on
a half-sided gate through which the tractor could drive. The field tests were
carried out on a farm in Bavaria, Germany, during tillage. During the field
tests, other parameters such as soil moisture and wind speed were controlled,
as these significantly affect dust development. We validated our dataset with
contrast enhancement and image dehazing algorithms and analyzed the
generalizability from recordings from the moving tractor. Finally, we
demonstrate the application of dust removal based on a high-level vision task,
such as person classification. Our empirical study confirms the validity of
RB-Dust for vision-based dust removal in agriculture.
RB-Dust -- A Reference-based Dataset for Vision-based Dust Removal
http://arxiv.org/abs/2306.07244v1