Towards sensor agnostic artificial intelligence for underwater imagery
Miquel Massot‐Campos, Takaki Yamada, Blair Thornton · 2023
Underwater images in global datasets are gathered by different cameras and under different lighting and altitude conditions. Image formation models can potentially compensate for these known differences and improve machine learning (ML) performance. In this paper we will investigate how ML trained on images from one system can classify images taken by another. We will train two ML classification models based on two different underwater camera systems. We are going to evaluate the performance of each model with data from both platforms and will demonstrate the improvement of using image formation models to make an image look-like it has been gathered with a different system and how ML performance is affected.