## 6 Experimental Results and Discussion
Nevertheless, it is important to stress that increasing indiscriminately the amount of images from the presented method will introduce additional noise, similar to what can be observed  
Figure 6: Sample of correct instant detections over frames obtained from the CCMAR footage. Top row: correct detections. Middle row: partially correct detections. Bottom row: failed cases. in Figure 6, although, in comparison with the OzFish dataset metrics, there was a significant increase across almost all metrics, specifically the mAP\({}_{75}\) and the recall. Notwithstanding, the obtained results are evaluated against the generated dataset at this stage; therefore, this analysis of these metrics must be evaluated in between the same data; however, a formal ground-truth evaluation is presented in Table 2.  
Evaluating the effects of network input image size variation, an increase was observed in the mAP\({}_{50}\) of 0.84% from 91.90%, followed by an increase of 0.37%, while the mAP\({}_{75}\) rose 3.71% from 68.96%, and afterwards 2.03%. The variation of the IoU at 50% was also minimal, increasing 0.97% from 73.31%, and 0.82%. The precision only increased 1% from 88% when changing the network size from 512 \(\times\) 512 to 608 \(\times\) 608, while the recall also increased 1% from 83% when the network varied from 416 \(\times\) 416 to 512 \(\times\) 512. The number of images used for this evaluation was 35,510, as it had provided the higher results previously. The obtained results demonstrate a small decay in mAP and IoU while reducing the network input image size, which confirms the potential of the presented method to automatically generate a robust and efficient distilled dataset adapted for the deployment of smaller networks, such as with edge devices. Therefore, a smaller version of the YOLOv4 was trained, the YOLOv4-tiny, with two YOLO layers, and the YOLOv4-tiny-3l, with the original three YOLO layers. In comparison, the YOLOv4 model has 137 pre-trained convolutional layers, while the YOLOv4-tiny has 29 pre-trained convolutional layers. The network image size varies from 416 for the smaller network to 608 for the head with three YOLO layers. Using the same amount of images, a mAP\({}_{50}\) of 85.00% and 89.66%, respectively, was observed, as well as a significant increase in the mAP\({}_{75}\), with 45.33% and 61.02%. The IoU at 50% varied from 68.74% to 73.69%, while the precision varied from 87% to 90%, and the recall varied from 71% to 73%. Comparing these results to the obtained while using the complete YOLOv4 complete model, there is a decay in the performance at higher IoU, which is also expressed in a lower recall. Nevertheless, these outcomes have proven the viability of the proposed method for smaller neural networks aimed at edge deployment, especially considering the high mAP\({}_{50}\). To further evaluate the performance and adaptive generalization of the proposed method, the trained models were evaluated on the unseen image collection annotated by a CCMAR marine biologist; these results are presented in Table 2. The first two models were trained with a network input image size of 608 and the full YOLOv4 model layers, with the first trained on the OzFish dataset and the second on the automatically generated CCMAR dataset. Comparing the two models, a significant increase in the mAPs was observed, with the mAP\({}_{50}\) rising 25.45% from 48.60% to 74.05%, and the mAP\({}_{75}\) increasing 15.84% from 27.56% to 43.40%. Curiously, the IoU decreased 3.93% from 76.22%; the same also occurred to precision, decreasing 4% from 94%. However, the recall increased 14% from 35% to 49%. These results confirm a higher accuracy using the proposed method with a baseline noisy dataset to refit and automatically generate a new dataset directly associated with the desired final application. The lower IoU can derive from the observed issue of an additional annotation space after the fish's tail.