## 4 Data
### Transfer Learning  
The YOLOv4 architecture [5] was adopted for the implementation of pipeline (g). The main advantage of using the YOLO architecture is the balance between its fast detection rate, by design, and the high accuracy maintained, even across complex scenes, while requiring moderate computational power. Furthermore, since the whole image is considered either during the training or inference phases, the additional contextual information contributes to a lower false-positive rate, although, due to the removal of the region proposal step, some objects, specifically smaller objects, can fail to be localized [62]. It is important to stress at this moment that the YOLOv4 is used across the different steps of the method, enabling  
Figure 4: Overall pipeline of the proposed method. Each stage is identified by (\(*\)). the possibility of the detection of multiple planes of distance, extensive environmental and target generalization, various input image resolutions, and a further reduction of type I and II errors. The baseline-trained model follows the traditional transfer learning object detection training algorithm, where the weights are already initialized from previous training, resorting to higher computational power requirements on a major carefully curated dataset. The transfer learning technique [13] grants the advantage of a faster model training, requiring fewer resources while achieving similar accuracy and performance results to the original training models. In the present case, the initial YOLOv4 network was previously trained on the Common Objects in Context (MS-COCO) dataset [61], which consists of 80 classes distributed through more than 118 k images for training, and 5 k images for validation. Arguably, the use of a multi-class network for the object detection task of detecting one class seems unreasonable, although the targeted class for the purpose--fish--contains multiple species, with distinct peculiarities across them. Furthermore, isolating the detection and classification stages aims at providing, at a later stage, a crucial element for the recognition of invasive fishing species. The default training configuration of the baseline model using the YOLOv4 network architecture was altered to detect only a single class named \"fish\", and the input three color channel images remained at 608 \(\times\) 608 pixels. The OzFish dataset images were divided into training, validation, and test subsets, following a ratio of distribution of 80%, 15%, and 5%, respectively, for a total of 1753 images. The CCMAR _negative sample_ was added, proportional to the number of original images from the OzFish dataset, i.e., 1753 images were retrieved from the overwater and transition states of the original CCMAR videos. Additional default hyperparameters were altered as follows: the number of anchor boxes remained the same, although their values were recalculated to improve small object detection, either on a further plane or on smaller species, replacing the initial MS-COCO dataset values by pairs of width and height as: 16 \(\times\) 25, 37 \(\times\) 50, 73 \(\times\) 72, 61 \(\times\) 137, 125 \(\times\) 119, 123 \(\times\) 265, 209 \(\times\) 184, 276 \(\times\) 331, and 483 \(\times\) 518. The batch size was set as 128, with the subdivisions set at 16. Even though using a large batch size affects negatively the model generalization [63], it became necessary, as this noisy dataset, after multiple initial training, was unable to converge to the global minima. The maximum number of iterations was set to 10k, with steps of 8k and 9k; the learning rate was adjusted to 0.005, while the decay remained at 0.0005 and the momentum at 0.949.