An underwater observation dataset for fish classification and fishery assessment
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Abstract
Using Dual-Frequency Identification Sonar (DIDSON), fishery acoustic observation data was collected from the Ocqueoc River, a tributary of Lake Huron in northern Michigan, USA. Data were collected March through July 2013 and 2016 and included the identification, via technology or expert analysis, of eight fish species as they passed through the DIDSON’s field of view. A set of short DIDSON clips containing identified fish was curated. Additionally, two other datasets were created that include visualizations of the acoustic data and longer DIDSON clips. These datasets could complement future research characterizing the abundance and behavior of valued fishes such as walleye (Sander vitreus) or white sucker (Catostomus commersonii) or invasive fishes such as sea lamprey (Petromyzon marinus) or European carp (Cyprinus carpio). Given the abundance of DIDSON data and the fact that a portion of it is labeled, these data could aid in the creation of machine learning tools from DIDSON data, particularly for invasive sea lamprey which are amply represented and a destructive invader of the Laurentian Great Lakes.
| Publication type | Article |
|---|---|
| Publication Subtype | Journal Article |
| Title | An underwater observation dataset for fish classification and fishery assessment |
| Series title | Scientific Data |
| DOI | 10.1038/sdata.2018.190 |
| Volume | 5 |
| Year Published | 2018 |
| Language | English |
| Publisher | Nature |
| Contributing office(s) | Great Lakes Science Center |
| Description | Article number: 180190; 8 p. |
| First page | 1 |
| Last page | 8 |
| Google Analytic Metrics | Metrics page |