Labeled images of emerged salmonids in a riverine environment

Abstract

Objectives

These data enable the development of machine learned models to detect unintended passage of salmonids over in-stream barriers. Such models are key to fully characterizing the effectiveness of selective passage systems, as they detect and quantify fish passage which occurs outside of the intended transit passage and selection mechanism. These data were used to construct custom surveillance tools for FishPass (https://www.glfc.org/fishpass.php), a 20-year restoration project to provide selective up- and down-stream passage of desirable fishes while simultaneously blocking or removing undesirable fishes.

Data description

The datasets contain over 2300 annotated images of emerged salmonids collected in a natural riverine environment. The images stem from surveillance video collected during 2022 and 2023 fall runs of several pacific salmonid species introduced to the Laurentian Great Lakes on the Boardman (Ottaway) River in Traverse City, MI, USA. In addition to images of fully emerged salmonids, datasets are provided containing images of partially emerged salmonids, fully submerged fish, and other wildlife present in a riverine environment. The environmental conditions represented by most of the images were clear or partly cloudy. These datasets could be used to develop custom object detection models for emerged fish in riverine environments.