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Camera-based electronic monitoring (EM) systems are increasingly beingdeployed onboard commercial fishing vessels to collect essential data forfisheries management and regulation. These systems generate large quantities ofvideo data which must be reviewed on land by human experts. Computer vision canassist this process by automatically detecting and classifying fish species,however the lack of existing public data in this domain has hindered progress.To address this, we present the Fishnet Open Images Database, a large datasetof EM imagery for fish detection and fine-grained categorization onboardcommercial fishing vessels. The dataset consists of 86,029 images containing 34object classes, making it the largest and most diverse public dataset offisheries EM imagery to-date. It includes many of the characteristic challengesof EM data: visual similarity between species, skewed class distributions,harsh weather conditions, and chaotic crew activity. We evaluate theperformance of existing detection and classification algorithms and demonstratethat the dataset can serve as a challenging benchmark for development ofcomputer vision algorithms in fisheries. The dataset is available athttps://www.fishnet.ai/.

The Fishnet Open Images Database: A Dataset for Fish Detection and Fine-Grained Categorization in Fisheries