The Fishnet Open Images Database: A Dataset for Fish Detection and Fine-Grained Categorization in Fisheries
This dataset addresses a domain-specific problem for fisheries management and regulation by providing a public resource to assist in automating fish species detection and classification from video data.
The authors tackled the lack of public data for computer vision in fisheries by presenting the Fishnet Open Images Database, a large dataset of 86,029 images with 34 object classes for fish detection and fine-grained categorization from electronic monitoring systems, and demonstrated its use as a challenging benchmark for existing algorithms.
Camera-based electronic monitoring (EM) systems are increasingly being deployed onboard commercial fishing vessels to collect essential data for fisheries management and regulation. These systems generate large quantities of video data which must be reviewed on land by human experts. Computer vision can assist 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 dataset of EM imagery for fish detection and fine-grained categorization onboard commercial fishing vessels. The dataset consists of 86,029 images containing 34 object classes, making it the largest and most diverse public dataset of fisheries EM imagery to-date. It includes many of the characteristic challenges of EM data: visual similarity between species, skewed class distributions, harsh weather conditions, and chaotic crew activity. We evaluate the performance of existing detection and classification algorithms and demonstrate that the dataset can serve as a challenging benchmark for development of computer vision algorithms in fisheries. The dataset is available at https://www.fishnet.ai/.