Overview of the Fishnet Open Images Database
The Fishnet Open Images Database is a dataset of images from on-board monitoring cameras that have been annotated with object bounding boxes. The current dataset is composed entirely of long line tuna fishing activity in the Western and Central Pacific. In the future we anticipate contributions from other fisheries that could benefit from having AI algorithms improve their workflows.
The v0.1.2 dataset contains 159,119 bounding boxes for 28 object classes (composed entirely of fish species and humans) on ~35K images with an average of 5 bounding boxes per image. We've also included a higher order label that groups fishes that lack a sufficient number of examples (< 100). The boxes have been manually drawn by professional annotators at Samasource to ensure accuracy and consistency. We are principally interested in annotating fish, however, there is value in annotating humans as well as they can be a proxy for fishing activity. For fishes, we define a fish feature or part (e.g. fins, tail, head) if it is ≥80% in the frame. We label both fish on the deck of the vessel and fish that are recognizable as fish alongside the vessel in the water. We also annotate bounding boxes around ‘human features’ that are not faces, such as boots, legs, and arms.
In processing both video and declaration data we remove any information related to location, vessel name, unique identifying features like vessel license number, and any frames with human faces.
The v0.1.2 dataset is relatively small and lacks diversity as it covers a limited number of vessels and individual trips and includes many sequences. The dataset will be actively maintained. Subsequent releases will include training, test, and validation sets.
The dataset was compiled by The Nature Conservancy in partnership with Satlink, Archipelago Marine Research, the Pacific Community, the Solomon Islands Ministry of Fisheries and Marine Resources, the Australia Fisheries Management Authority, and the governments of New Caledonia and Palau.