  Computer vision techniques are on the rise for industrial applications, like
process supervision and autonomous agents, e.g., in the healthcare domain and
dangerous environments. While the general usability of these techniques is
high, there are still challenging real-world use-cases. Especially transparent
structures, which can appear in the form of glass doors, protective casings or
everyday objects like glasses, pose a challenge for computer vision methods.
This paper evaluates the combination of transparent objects in conjunction with
(naturally occurring) contamination through environmental effects like hazing.
We introduce a novel publicly available dataset containing 489 images
incorporating three grades of water droplet contamination on transparent
structures and examine the resulting influence on transparency handling. Our
findings show, that contaminated transparent objects are easier to segment and
that we are able to distinguish between different severity levels of
contamination with a current state-of-the art machine-learning model. This in
turn opens up the possibility to enhance computer vision systems regarding
resilience against, e.g., datashifts through contaminated protection casings or
implement an automated cleaning alert.
