The oceans and other marine ecosystems are indispensable to life, so the understanding and knowledge of their biodiversity is crucial to the use of their resources and exploration. These environments are complex and difficult to access, so different types of remote sensing technologies are used to study them. These intelligent sensors can collect a massive amount of data, which, once reviewed and analyzed, can help to draw conclusions and increase knowledge of these underwater environments. Manually reviewing and organizing through this large amount of information is both time-consuming and costly. Therefore, it is advisable to employ automated techniques from machine learning and deep learning fields. In recent years, these methods have proven to be efficient and have obtained very good results in solving different problems applied to the marine world: image enhancement, image classification, segmentation and object detection. This paper presents a systematic review, conducted in accordance with the PRISMA 2020 guidelines, aimed at summarizing the methods used to address underwater problems and their reported results.
López-Vázquez et al. (Thu,) studied this question.