## 2 Related Work
The availability of the OzFish dataset [12] increased the published videos and annotated bounding boxes to the order of thousands. Additional segmentation annotations were presented on the DeepFish dataset [26], which also introduced marine videos under different conditions. The SUIM dataset [27] focused on the semantic segmentation of underwater images, while the NorFisk dataset [28] focused on fish farms. Current DL object detection methods are divided into two main approaches, the one-stage detectors [5,14] and the two-stages detectors [29,30]. The idea behind two Gaussian mixture models (GMM) and optical flow, and a CNN to train an R-CNN network, achieving an F-score, i.e., the weighted harmonic mean between precision and recall, of 87.44% and 80.02%, accordingly. [PERSON] et al. [51] also evaluated the published findings with the LifeCLEF15 dataset and additionally a custom dataset; similarly, the temporal information used the previously proposed methods, although a parallel YOLO network was applied, combining the proposed location and classification at a later stage, increasing the F-score on the LifeCLEF15 to 95.47%, and achieving a classification accuracy of 91.64%, while obtaining 91.2% detection accuracy on the custom dataset, and 79.8% on classification. [PERSON] et al. [52] published the Brackish dataset, comparing the performance of the YOLOv2 and YOLOv3 through the Intersection over Union (IoU) metric, achieving with the latter a \(\text{mAP}_{\text{IoU}=0.50}\) of 83.72%, and a more challenging \(\text{mAP}_{\text{IoU}=0.50\text{.}05\text{. }095}\) of 39.83%. [PERSON] et al. [19] also evaluated the previous dataset with the YOLOv4 [5] network, the tiny YOLOv4 [53] architecture, and their proposed YOLOv4 modified network with MobileNetv2 [54] as the backbone, achieving a mAP of 93.56%, 80.16%, and 92.65%, accordingly, while reducing the necessary model parameters. [PERSON] and [PERSON] [55] suggest an ensemble of two-stage object detection networks connected by long short-term memory networks through cascade structures applied to a custom challenging dataset containing variable quantities of small-scale objects, obtaining an average of 60% on both precision and recall metrics. [PERSON] et al. [18] proved the reliability of DL approaches for underwater monitoring, comparing the results obtained from the implementation of a Mask R-CNN [56] network on a custom dataset against an analysis from marine experts and citizen scientists, recording an increase in the abundance detection of 7.1% and 13.4% for single images, and 1.5% and 7.8% for videos, respectively. [PERSON] et al. [11] used the F4K dataset to train a pre-trained classifier and further train with a limited custom dataset. The detection and classification tasks were divided, with the YOLOv3 [36] architecture responsible for the object detection stage and a Squeeze-and-Excitation [57] architecture employed for classification. A mAP of 86.96% was obtained for the object detection task, while the classification achieved an accuracy of 99.27% on the F4K dataset and 83.68% on the custom dataset. [PERSON] et al. [9] also implemented the YOLOv3 on a limited annotated dataset, retaining the detections above 25% confidence score and manually correcting the predictions for further network retraining, achieving a mAP of 88.09%. [PERSON] et al. [58] created combinations of five diverse custom datasets obtained from reef and seagrass footage from different locations, and trained an implementation of Mask R-CNN to demonstrate the improved generalization across different habitats. As it can be seen, previous works have approached the integration of DL-based object recognition techniques for autonomous underwater monitoring by either focusing on improving state-of-the-art results across public datasets, implementing novelty methods to underwater conditions and comparing performance evolutions, or building localized custom datasets.