## 2 Related Work  
The first records of the use of video underwater date back to the 1950s, with an increased use of remote solutions during the 2000s [6], removing or reducing the presence of humans underwater. Regardless of the visual limitations introduced by the nature of our oceans, the use of underwater footage for marine monitoring, either streaming or recording, remains a viable and necessary solution. Recently, the conjunction of a technological evolution marked the beginning of a new age in artificial intelligence (AI) techniques: the maturation of image sensors offer higher resolutions, sharper images, and increased dynamic ranges, which are essential to low-light applications, such as in underwater environments; the exponential expansion of computational power available, in particular the parallelization ability of graphical processing units (GPU), and the development of advanced mathematical libraries that revolutionized modern science and research, simplifying complex tasks while offering the ability to solve advanced challenges. The synergy of these technological advances converges in the separation of traditional and modern CV [7; 15], introducing an expeditious development in underwater monitoring applications. Considering the diversified nature of marine environments, the application of traditional CV proved to be convoluted and impractical, requiring static and adequate image conditions to perform classical CV algorithms [16]. With the emergence of DL techniques and their inherent versatility and performance, multiple complex tasks were finally efficiently achieved, replacing the previous state of the art and swiftly approaching metrics similar to human performance--even surpassing it [17; 18]. However, the CV object detection performance accomplishments, even while achieving exceptional generalization, endure different limitations and variability while performing underwater [19]. Nevertheless, while the creation of a universal marine classifier and object detector represents colossal challenges, narrow applications of DL methods to monitor local marine fauna proved to be successful [7]. A crucial requirement for the application of DL methods is data; specifically, properly annotated datasets [20]. Subsequently, the performance of any trained DL model is directly related to the chosen input data, either by its quality or quantity. Notwithstanding the progress achieved through preprocessing procedures, data augmentation, tuning hyperparameters, or fine-tuning convolutional neural networks, the base outcome remains directly correlated to the trained dataset [21]. Properly annotated data results from the combination of dedication, skill, and time. The initial approach to data gathering bifurcates into a question of generalization or applicability. Therefore, during the first stage of a custom object detection project, the abstractly chosen data, prior to the labeling phase, can already influence and bias the output model [21]. However, distinct applications may require generalization, while static underwater monitoring stations may benefit from fitted models. Underwater environmental biodiversity raises a convoluted obstacle to the generation of a universal dataset to observe and analyze marine ecosystems. Therefore, available underwater visual datasets [2], either for classification or object detection, normally represent local marine visual conditions, restricting an attainable application to dissimilar underwater areas. A landmark underwater dataset aimed at the study of marine ecosystems is the Fish4 Knowledge dataset (F4K) [22], was obtained from the capture of live video in the open sea. The ImageCLEF initiative introduced the LifeCLEF14 and LifeCLEF15 datasets [23], increasing the number of annotated videos containing fish, while also resorting to the F4K dataset. Focusing on the task of fish recognition, the WildFish and WildFish++ datasets [24; 25] made available hundreds of thousands of images containing thousands of fish species.