Deep learning‐based seabird detection in fisheries for seabird protection

New Zealand is considered to be the 'seabird capital' of the world. As part of the harvesting process, some commercial fishers accidentally bycatch seabirds during fishing operations, which can result in accidental deaths and injuries. The accidental bycatch is impacting the long-term sustainability of New Zealand seabird populations. To address this, we developed a YOLO model that can be used to automatically detect seabirds that interact with the fishing vessels. The model development process involved gathering, annotating and preprocessing a new image dataset, conducting transfer learning across YOLO benchmark models, and performing hyperparameter tuning on the top YOLO models to further improve the model's performance. We evaluate the performance and effectiveness of our developed model under diverse data conditions, with it achieving a mAP@50 score of 0.9926 and a mAP@50-95 score of 0.9147 on the test data. The results demonstrate that the developed model performs effectively in unconstrained real-world marine scenarios, addressing the limitations of previous models primarily evaluated in controlled settings. This automation could help to reduce or even eliminate manual inspection of footages by reviewers and will help to quantify seabird interactions with commercial fishing vessels. Our contributions represent a significant first step in automated seabird detection, mitigating the gap between constrained and unconstrained real-world maritime scenarios.