Elasmobranchs (sharks and rays) can be important components of marine ecosystems but are experiencing global population declines. Effective monitoring of these populations is essential to their protection. Baited Remote Underwater Video Stations (BRUVS) have been a key tool for monitoring, but require time-consuming manual analysis. To address these challenges, we developed SharkTrack, an AI-enhanced BRUVS analysis software. SharkTrack uses Convolutional Neural Networks and Multi-Object Tracking to detect and track elasmobranchs and provides an annotation pipeline to manually classify elasmobranch species and compute MaxN, the standard metric of relative abundance. We tested SharkTrack on BRUVS footage from locations unseen by the model during training. SharkTrack computed MaxN with 89% accuracy over 207 hours of footage. The semi-automatic SharkTrack pipeline required two minutes of manual classification per hour of video, a 97% reduction of manual BRUVS analysis time compared to traditional methods, estimated conservatively at one hour per hour of video. Furthermore, we demonstrate SharkTrack application across diverse marine ecosystems and elasmobranch species, an advancement compared to previous models, which were limited to specific species or locations. SharkTrack applications extend beyond BRUVS analysis, facilitating rapid annotation of unlabeled videos, aiding the development of further models to classify elasmobranch species. We provide public access to the software and an unprecedentedly diverse dataset, facilitating future research in an important area of marine conservation.

翻译：板鳃类动物（鲨鱼和鳐鱼）是海洋生态系统的重要组成部分，但其种群数量正经历全球性衰退。对这些种群的有效监测对其保护至关重要。诱饵远程水下视频站（BRUVS）已成为关键监测工具，但需要耗时的人工分析。为应对这些挑战，我们开发了SharkTrack——一款基于人工智能增强的BRUVS分析软件。SharkTrack采用卷积神经网络（CNN）与多目标跟踪技术实现板鳃类动物的检测与追踪，并提供标注流程以人工分类板鳃类物种并计算相对丰度标准指标MaxN。我们在训练阶段未接触过的地域采集的BRUVS影像上测试了SharkTrack。该软件在207小时影像中计算MaxN的准确率达到89%。半自动化的SharkTrack流程每小时视频仅需两分钟人工分类，与传统方法（保守估计每小时视频需一小时人工分析）相比，BRUVS人工分析时间减少97%。此外，我们展示了SharkTrack在不同海洋生态系统及板鳃类物种间的应用能力，这相较于以往局限于特定物种或地域的模型具有显著进步。SharkTrack的应用不仅限于BRUVS分析，还可加速未标注视频的标注流程，助力开发更完善的板鳃类物种分类模型。我们公开提供该软件及前所未有的多样化数据集，以推动这一重要海洋保护领域的未来研究。