## 4 Data  
As already mentioned, datasets are a crucial part of the modern DL object detection method, where the quality of the annotations, scenes, and object variability, as well as the appropriate and balanced quantity of samples, are pivotal for the training of unbiased and functional models. This direct correlation can be observed between the evolution of two landmark large object detection datasets, the PASCAL Visual Object Classes Challenge [60], and the MS-COCO dataset [61], with the latter increasing the number of available images, annotations, classes, and, more importantly, the presence and density of small objects, approaching the object distributions closer to the real world [17]. Therefore, the data collected and annotated should reflect similar guidelines, either in location precision or object density, to maintain a uniform level of labeling quality. The demand for creating custom datasets emerges from the absence of the targeted classes on available datasets, the unreliability of noisy datasets, and performance improvement limitations caused by constrained localization or for specific controlled applications. However, the acquisition and proper labeling of data for the generation of a reliable dataset is a tedious and time-consuming process, which is especially dependent on the annotator's skills, attention, and endurance. Nevertheless, several approaches were proposed to expedite the annotation process, focusing primarily on expanding limited annotated data to automatically annotate unlabeled data, i.e., pseudo-labeling. Figure 1: Diagram presenting the overall structure of the underwater monitoring station prototype, with the underwater monitoring station connected to the land station by submarine optic fiber and power cables. Regarding object detection, three mechanisms are usually chosen: a semi-supervised learning approach, exploring the relation between labeled and unlabeled data; an active learning approach, which automatically selects the next most relevant unlabeled sample, optimizing the annotation process; and the automatic generation of a dataset based on a pre-trained model, which relies profoundly on the ability to properly generalize unseen data. Nonetheless, these methods are primarily suitable for the classification task, while still empirical for the object detection task. In order to test the model presented in this paper, underwater footage from the Algarve seabed in Portugal was gracefully provided by the Algarve Centre of Marine Sciences (CCMAR). This data was obtained using BRUV techniques, consisting of 21 uncut and unlabeled videos from different locations, as can be observed in Figure 2, containing diverse habitats and environmental conditions, which is either a generalization challenge or a baseline solution [58]. The gathered recordings have an average duration of 33 min each, summing to a total of 695 min, while still containing initial parts onboard the research vessels, and random endings, which are either underwater, transitioning, or back onboard. A collection of 383 unseen images from different areas were annotated by a marine biologist, providing a ground-truth test evaluation. Figure 2 shows in the top row samples (frames) of CCMAR unlabeled footage in different environments, in an _underwater stage_. This targets the sector of the video where the camera is stable on the seabed. In the middle row, it is the same as previously, but with the ground-truth annotations performed by a marine biologist. In the bottom row, _negative sample_ frames are obtained from the video that do not occur underwater, i.e., the _overwater stage_, where the camera is out of the ocean, for the two leftmost images, and the _transition stage_ for the two rightmost images. The transition state focuses on the phase where the camera is being dropped or pulled onboard. This process of overwater and transition footage is completely automated, but it is out of the focus of the present paper.