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SafeSea: Synthetic Data Generation for Adverse & Low Probability Maritime Conditions by Tran, Martin; Shipard, Jordan; Mulyono, Hermawan; Wiliem, Arnold; Fookes, Clinton is a scholarly article available to read on EtoBox.

What is SafeSea: Synthetic Data Generation for Adverse & Low Probability Maritime Conditions about?

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 accord

Author

Tran, Martin; Shipard, Jordan; Mulyono, Hermawan; Wiliem, Arnold; Fookes, Clinton

Published

2023

Language

EN