  Autonomous vehicles use cameras as one of the primary sources of information
about the environment. Adverse weather conditions such as raindrops, snow, mud,
and others, can lead to various image artifacts. Such artifacts significantly
degrade the quality and reliability of the obtained visual data and can lead to
accidents if they are not detected in time. This paper presents ongoing work on
a new dataset for training and assessing vision algorithms' performance for
different tasks of image artifacts detection on either camera lens or
windshield. At the moment, we present a publicly available set of images
containing $8190$ images, of which $3390$ contain raindrops. Images are
annotated with the binary mask representing areas with raindrops. We
demonstrate the applicability of the dataset in the problems of raindrops
presence detection and raindrop region segmentation. To augment the data, we
also propose an algorithm for data augmentation which allows the generation of
synthetic raindrops on images. Apart from the dataset, we present a novel
gradient-based algorithm for raindrop presence detection in a video sequence.
The experimental evaluation proves that the algorithm reliably detects
raindrops. Moreover, compared with the state-of-the-art cross-correlation-based
algorithm \cite{Einecke2014}, the proposed algorithm showed a higher quality of
raindrop presence detection and image processing speed, making it applicable
for the self-check procedure of real autonomous systems. The dataset is
available at
\href{https://github.com/EvoCargo/RaindropsOnWindshield}{$github.com/EvoCargo/RaindropsOnWindshield$}.
