## 3 Results
### Comparision of Different Fishing Detection Methods  
In this section, we conducted a comparative analysis of fishing monitoring using visual examination, the VBD algorithm, and computer vision techniques. During the peak fishing season (June and July) of 2020, visual inspection successfully identified operational fishing vessels over a span of 60 days. Only one day (4 th June) yielded unrecognizable images (Figure 5, type 3). On 29 days, the imagery allowed for the identification and precise location of most fishing vessels (Figure 5, type 1). However, on the remaining 31 days, although fishing vessels were visually confirmed, their exact positions could not be determined, or some vessels were entirely obscured (Figure 5, type 2, and Figure A3). We then analyzed the results obtained from both the VBD threshold algorithm and computer vision models. Even though these light-based high-seas fishing vessels operated daily throughout the fishing season, a consistent pattern of variation emerged across the VBD algorithm and the three computer vision models (Figure 5). The highest vessel counts detected by all three methods corresponded with clear visual images, while lower counts were associated with blurred images. These high counts were also comparable to the AIS-derived ground truth, with maximum vessel numbers reaching approximately 80 (Figure 5). Utilizing the M16 parameter as an indicator of meteorological conditions, the findings further demonstrated that fluctuations in the number of detected fishing vessels corresponded with changes in M16 (Figure 5). In terms of overall counts, the VBD algorithm detected approximately 2000 vessels throughout the fishing season, while the YOLO-based and related computer vision algorithms observed a 25% increase, identifying around 2500 vessels. In cases where location and identification were largely feasible, we conducted a comparative analysis of various methods against the results of visual inspection (Figure 6). The validation findings revealed that the VBD algorithm produced the least accurate results compared with the visually derived ground truth (R\({}^{2}\) = 0.64). In contrast, the regression consistency between the YOLO methods and visual inspection was stronger than that of the VBD algorithm. YOLOv8 and its improved versions showed significant correlation, with YOLOv8 demonstrating slightly lower precision (R\({}^{2}\) = 0.82), while the two refined models achieved comparable and superior results (R\({}^{2}\) = 0.97). For clearly visible targets, the results of the VBD algorithm closely aligned with those of the computer vision models (Figure A1). However, in instances where the targets were indistinct but still locatable, computer vision \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & **Size** & **Parameters (MB)** & **mAP** & **FPS (s)** & **GFLOPS** \\ \hline \multirow{8}{*}{YOLO-P2} & n & 2.92 & 0.970 & 138.89 & 12.2 \\  & s & 10.63 & 0.971 & 107.53 & 36.6 \\  & m & 25.03 & 0.965 & 49.75 & 97.9 \\  & l & 42.82 & 0.968 & 45.05 & 204.7 \\  & x & 66.56 & 0.969 & 30.67 & 316.1 \\ \hline \multirow{8}{*}{YOLO-SPD} & n & 2.86 & 0.973 & 73.53 & 45.1 \\  & s & 10.54 & 0.957 & 39.84 & 164.7 \\ \cline{1-1}  & m & 24.85 & 0.949 & 21.55 & 423.9 \\ \cline{1-1}  & l & 42.29 & 0.953 & 13.11 & 843.3 \\ \cline{1-1}  & x & 66.07 & 0.956 & 8.32 & 1317 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Parameters, mAP, FPS, and GFLOPS of improved YOLO models at different scales. outperformed the VBD threshold algorithm, effectively extracting these more ambiguous instances (Figure 10). Further detailed analysis showed that both the YOLO-P2 and YOLO models produced more false detections from the background compared with YOLO-SPD, with SPD demonstrating overall better performance. Although the VBD and computer vision methods differed in their precision of extraction, the monthly distribution results revealed a consistent trend across both methods (Figure 7).