## 4 Discussion
### Opportunities for High-Seas Lit Fishing Detection with Computer Vision  
Maritime lights captured in nightly imagery may be influenced by cloud cover and lunar phases [39]. Particularly when both cloud cover and moonlight exposure are at play, small-power maritime lights can be easily obscured [11]. However, under conditions of thin cloud cover, the bright lights of industry lit fishing vessels can still be effectively located and distinguished (Figure 2). Our visual observations indicate that during the fishing season,these very bright squid fishing vessels can still be effectively located and distinguished under thin cloud conditions (Figure 2). Nevertheless, the visual detection method is both time-consuming and labor-intensive and is susceptible to subjective judgments. Achieving automated accurate identification to a level of interpretability comparable to visual results is the main challenge in extracting and monitoring these industry fishing vessels. Under cloudless and moonless conditions, the threshold method performs exceptionally well in extraction and recognition [10; 17]. However, under thin cloud conditions, a fixed threshold may not be effective for extraction and recognition. To extend the applicability of the threshold method, EOG has proposed an adaptive threshold approach to address issues under various meteorological conditions. Yet, to avoid false detections caused by moonlit clouds, the threshold is set relatively high, which may result in some situations that are still observable under visual conditions being unable to be extracted by traditional threshold methods. Our research findings demonstrate the advantages of computer vision technology over traditional methods, effectively enhancing the accurate extraction capability of industry fishing vessels under thin cloud conditions. The robust capabilities and swift progress in computer vision technology present a wealth of opportunities for the precise extraction, monitoring, and study of fishing vessels using lights. Visual interpretation adeptly decodes the majority of lighting images within the research area. Cloudy conditions, despite their challenges, cannot obscure the discernible anomalies in nightly imagery, which still offer a broad evaluation of the operational zones and vessel positions (Figure A3). In many cases, the dynamics and shifts within fishing grounds do not require a count of the fishing vessels; pinpointing the operational areas is itself a crucial piece of information for such studies. Nonetheless, current methodologies fall short in interpreting these images, highlighting a notable challenge for the future of automated extraction and monitoring in designated research fishing zones. Additionally, in the South Atlantic Anomaly region, another high-seas lit fishing \"hot spot\", the interference of high-energy particles can lead the standard maritime imagery threshold method astray, misidentifying these particles as fishing vessels and thus generating a significant number of false positives [40]. The differentiation between high-energy particles and genuine fishing vessels is beyond the scope of threshold methods alone [41]. Computer vision, leveraging its formidable prowess in classification, extraction, and recognition, offers the potential to significantly bolster the capabilities for extracting and monitoring light fishing vessels in this region.