  High-quality training data is essential for enhancing the robustness of
object detection models. Within the maritime domain, obtaining a diverse real
image dataset is particularly challenging due to the difficulty of capturing
sea images with the presence of maritime objects , especially in stormy
conditions. These challenges arise due to resource limitations, in addition to
the unpredictable appearance of maritime objects. Nevertheless, acquiring data
from stormy conditions is essential for training effective maritime detection
models, particularly for search and rescue, where real-world conditions can be
unpredictable. In this work, we introduce SafeSea, which is a stepping stone
towards transforming actual sea images with various Sea State backgrounds while
retaining maritime objects. Compared to existing generative methods such as
Stable Diffusion Inpainting~\cite{stableDiffusion}, this approach reduces the
time and effort required to create synthetic datasets for training maritime
object detection models. The proposed method uses two automated filters to only
pass generated images that meet the criteria. In particular, these filters will
first classify the sea condition according to its Sea State level and then it
will check whether the objects from the input image are still preserved. This
method enabled the creation of the SafeSea dataset, offering diverse weather
condition backgrounds to supplement the training of maritime models. Lastly, we
observed that a maritime object detection model faced challenges in detecting
objects in stormy sea backgrounds, emphasizing the impact of weather conditions
on detection accuracy. The code, and dataset are available at
https://github.com/martin-3240/SafeSea.
