Advancing Fisheries Research and Management with Computer Vision: A Survey of Recent Developments and Pending Challenges

The field of computer vision has progressed rapidly over the past ten years, with noticeable improvements in techniques to detect, locate, and classify objects. Concurrent with these advances, improved accessibility through machine learning software libraries has sparked investigations of applications across multiple domains. In the areas of fisheries research and management, efforts have centered on the localization of fish and classification by species, as such tools can estimate the health, size, and movement of fish populations. To aid in the interpretation of computer vision for fisheries research management tasks, a survey of the recent literature was conducted. In contrast to prior reviews, this survey focuses on employed evaluation metrics and datasets as well as the challenges associated with applying machine learning to a fisheries research and management context. Misalignment between applications and commonly used evaluation metrics and datasets mischaracterizes the efficacy of emerging computer vision techniques for fisheries research and management tasks. Aqueous, turbid, and variable lighted deployment settings further complicate the use of computer vision and generalizability of the reported results. Informed by these inherent challenges, culling surveillance data, exploratory data collection in remote settings, and selective passage and traps are presented as opportunities for future research.