Accelerating shark video surveys with AI and automated workflows

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Abstract
The capacity to monitor shark biodiversity is vital for conservation. Baited Remote Underwater Video Systems (BRUVS) are an increasingly popular and cost-effective method to characterize their biodiversity, abundance, and distribution. However, BRUV monitoring remains hindered by extensive manual video annotation and a lack of accessible tools to generate datasets for ecological analysis.To overcome these barriers, we developed a suite of automatic tools for shark detection and taxonomic classification. We integrated an object detection model (SharkTrack) with hierarchical species classification (Shark Detector) trained on 264,712 images of 309 shark species, in two open platforms—an R package (sharkDetectoR) and a lightweight desktop application (SharkByte)—making the updated model package an accessible tool across computational environments and user expertise.Tested on 46,332 holdout images and 13.9 hours of BRUV footage from the Main Hawaiian Islands and Palau, these tools achieved 92% species-level accuracy as well as 94.7% detection accuracy, and reduced annotation time by up to 90%.These findings underscore the value of combining scalable artificial intelligence to advance shark biodiversity monitoring. By substantially reducing manual annotation demands while maintaining high detection and classification performance, this framework offers a practical solution to speed up BRUV post-processing and filtering of digital observations. At the same time, it demonstrates automated methods as crucial tools for conservation and management, especially to address species-specific data deficiencies in shark research.