## 5 Conclusions  
This study introduced an advanced methodology for monitoring and extracting industry lit fishing vessels in specific fishing grounds, particularly under cloudy conditions, using computer vision techniques and nightly imagery. By leveraging the YOLO model and its enhanced variants, the proposed approach demonstrated a high accuracy in detecting fishing vessels in the central-east stock of the North Pacific, with improvements in precision and recall achieved through the small target detection enhancements model. The machine learning methods outperformed conventional threshold-based algorithms (VBD), particularly in handling blurred images affected by thin cloud cover, with the SPD model proving to be the most effective, closely aligning with visual inspection results and minimizing false detections. This research provides a reproducible framework that enhances the recognition of vessels in challenging conditions and offers valuable insights into the cloud cover characteristics of specific fishing grounds. Additionally, the study contributes a decade-long dataset for the North Pacific central-east stock fishing grounds, offering a valuable resource for further research and fisheries management.  
Figure 9: Comparison of VBD and our data flow for high-seas lit fishing monitoring.  
**Author Contributions:** Conceptualization, [PERSON] ([PERSON]) and Z.C.; data curation, [PERSON] ([PERSON]), [PERSON] ([PERSON]) and K.Z.; formal analysis, [PERSON] ([PERSON]); funding acquisition, Z.C.; methodology, [PERSON] ([PERSON]), [PERSON] ([PERSON]) and K.Z.; software, X.L.; validation, [PERSON] ([PERSON]) and [PERSON] ([PERSON]); visualization, [PERSON] ([PERSON]) and [PERSON] ([PERSON]); writing--original draft, [PERSON] ([PERSON]) and [PERSON] ([PERSON]); writing--review and editing, Z.C. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was supported by the National Natural Science Foundation of China (32303008) and the Central Public-interest Scientific Basal Research Fund, CAFS (Nos. 2024 RC07 and 2023 TD05).  
**Data Availability Statement:** The original contributions presented in the study are included in the article; further inquiries can be directed to the corresponding author.  
**Acknowledgments:** The authors would like to thank the anonymous reviewers for their very competent comments and helpful suggestions.  
**Conflicts of Interest:** The authors declare no conflicts of interest.  
Appendix AFigure 17: Typical blurry nightly images (**A**–C) and detections using various automated fishing extraction methods, (**D**–**F**) nightly images with detections by YOLO, (**G**–**I**) nightly images with detections by YOLO-P2, (**J**–**L**) nightly images with detections by YOLO-SPD, and (**M**–**O**) nightly images with detections by VBD.  
Figure A3: _Cont_.  
Figure 13: Satellite AIS-derived fishing vessels overlaid with typical obscured nightly images. While fishing locations can be roughly identified, the exact number of vessels cannot be determined. Panels (**A**,**C**,**E**,**G**) display typical obscured nightly remote sensing images, and panels (**B**,**D**,**F**,**H**) show AIS-derived fishing activity locations overlaid on nightly images.