## 3 Results  
### Fishing Detection with YOLO-Based Computer Vision Methods  
Three computer vision-based models were trained for the extraction and detection of central-east stock lit fishing vessels. For the validation set, YOLOV8 achieved its highest F1 score at a very low confidence (Conf) threshold, with a value of Conf = 0.004, resulting in an F1 score of 0.947. The two improved YOLO models exhibited a parabolic variation in their F1 scores. Specifically, the model designated as YOLO-SPD reached its peak F1 score at a confidence threshold of Conf = 0.182, while YOLO-P2 achieved its maximum at Conf = 0.247, with respective F1 scores of 0.977 and 0.964. Labeled images from 2020 served as the test set for evaluating the performance of these three models. The confidence thresholds for the test data were derived from the validation results at the point where the F1 scores peaked. Table 2 illustrates the performance parameters of the three detection models. The model validation outcomes demonstrated that both YOLOv8 and its enhanced counterparts were adept at efficiently filtering and extracting results under complex meteorological conditions, with high accuracy rates. YOLOv8 it \begin{table}
\begin{tabular}{c c} \hline \hline
**Item** & **Value** \\ \hline CPU & Intel(R) Xeon(R) E5-2686 v4 @ 2.30 GHz \\ RAM & 60 GB \\ GPU & NVIDIA RTX A4000 \\ Operating system & Ubuntu 20.04 \\ Cuda & CUDA 11.3 \\ Data processing & Python 3.9 \\ Deep learning framework & Pytorch 1.12.1 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Setup of the experiment environment. self achieved an accuracy of 89%, while the precision of both enhanced YOLOv8 models improved by approximately 5%, reaching around 93%. Representative nighttime imagery confirmed that all models were capable of fully and effectively detecting lit fishing vessels in the North Pacific under clear skies (Figures 4, A1 and A2). Comparatively, the original YOLOv8 model's performance in filtering is marginally less effective than its improved counterparts, particularly evident in a lower recall rate, leading to a higher likelihood of missed detections (Figures 4 and A1). In the test set, the original YOLOv8 architecture had a 21% omission rate for dense fishing boat targets. However, the integration of a small target detection head (YOLO-P2) and the Space-to-Depth (YOLO-SPD) model both significantly improved their performance in detecting fishing vessels, resulting in an effective increase in monitoring precision. The recall rate saw a notable increase, with both models improving by about 18%. Both enhancements effectively compensated for the original YOLOv8's tendency to miss detections. Regarding computational speed, the original YOLO architecture boasted the FPS. While the FPS of the improved models increased, the addition of the SPD model led to a more substantial enhancement compared with the addition of the detection head. Computational requirements followed a similar pattern, with YOLO having the lowest demand and YOLO-SPD significantly exceeding that of YOLO-P2 (Table 3). \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **P** & **R** & **F1** & **Confidence** \\ \hline YOLOV8 & 0.886 & 0.79 & 0.947 & 0.031 \\ YOLO-SPD & 0.932 & 0.987 & 0.977 & 0.182 \\ YOLO-P2 & 0.928 & 0.989 & 0.964 & 0.247 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The overall test results for YOLO and tiny targets improved models. Figure 4: Typical nighttime imagery and detection results from different methods: (**A**) Original nighttime image. (**B**) Detection results by YOLO, where missed targets are denoted by red arrows. (**C**) Detection results by YOLO-P2. (**D**) Detection results by YOLO-SPD. Beyond the n scale version, we also evaluated models of varying sizes for the improved YOLO models (Table 3). Contrary to expectations, the performance accuracy from the training results of the s, m, l, and x versions did not improve with the increase in model size.