Fish biomass, shape, and size determination

Patent metadata

Field	Value
Publication number	US-2023154225-A1
Application number	US-202318155485-A
Country	US
Kind code	A1
Filing date	Jan 17, 2023
Priority date	Jan 25, 2018
Publication date	May 18, 2023
Grant date	—

Abstract

Methods, systems, and apparatuses, including computer programs encoded on a computer-readable storage medium for estimating the shape, size, and mass of fish are described. A pair of stereo cameras may be utilized to obtain right and left images of fish in a defined area. The right and left images may be processed, enhanced, and combined. Object detection may be used to detect and track a fish in images. A pose estimator may be used to determine key points and features of the detected fish. Based on the key points, a three-dimensional (3-D) model of the fish is generated that provides an estimate of the size and shape of the fish. A regression model or neural network model can be applied to the 3-D model to determine a likely weight of the fish.

First claim

What is claimed is:

1 . A method for positioning one or more cameras within a fish pen, the method comprising:
receiving, at one or more computing devices, information identifying a type of fish housed within the fish pen; identifying one or more swimming characteristics associated with the type of fish; and positioning the one or more cameras within the fish pen in accordance with the one or more swimming characteristics.

2 . The method of claim 1 , wherein the one or more swimming characteristics include information identifying a swimming direction relative to a direction of water current, and the one or more cameras are positioned such that a longitudinal axis of a fish traveling along the swimming direction is substantially perpendicular to a view direction of the one or more cameras.

3 . The method of claim 1 , wherein the one or more swimming characteristics include information identifying a depth at which the type of fish prefers to swim, and the one or more cameras are positioned at the corresponding depth.

4 . The method of claim 1 , wherein the one or more swimming characteristics include information identifying a temperature range at which the type of fish prefers to swim, and positioning the one or more cameras comprises:
identifying a region within the fish pen where the water temperature is within the identified temperature range; and positioning the one or more cameras to image fish swimming through the identified region.

5 . The method of claim 1 , wherein the one or more swimming characteristics include information identifying an illumination range at which the type of fish prefers to swim, and positioning the one or more cameras comprises:
identifying a region within the fish pen where the amount of light is within the identified illumination range; and positioning the one or more cameras to image fish swimming through the identified region.

6 . The method of claim 1 , further comprising:
capturing at least one image using the one or more cameras positioned within the fish pen in accordance with the one or more swimming characteristics; identifying a fish within the at least one image; determining that an orientation of the fish relative to the camera does not satisfy a threshold condition; and adjusting corresponding locations of the one or more cameras responsive to determining that the orientation of the fish relative to the camera does not satisfy the threshold condition.

7 . The method of claim 1 , wherein the information identifying the type of fish is received at the one or more computing devices in the form of a user-input.

8 . A system comprising:
one or more cameras configured to capture underwater images in a fish pen; one or more computing devices operably coupled to a control system configured to control the one or more cameras; and one or more storage devices storing instructions which when executed by the one or more computing devices, cause the one or more computing devices to perform operations comprising:
receiving, at the one or more computing devices, information identifying a type of fish housed within the fish pen,
identifying one or more swimming characteristics associated with the type of fish, and
providing instructions to the control system for positioning the one or more cameras within the fish pen in accordance with the one or more swimming characteristics.

9 . One or more non-transitory computer-readable storage media comprising instructions, which, when executed by one or more computing devices, cause the one or more computing devices to perform operations comprising:
receiving, at one or more computing devices, information identifying a type of fish housed within a fish pen; identifying one or more swimming characteristics associated with the type of fish; and generating one or more instructions for positioning the one or more cameras within the fish pen in accordance with the one or more swimming characteristics.

10 . A method for uniquely identifying a fish within a fish pen, the method comprising:
obtaining, using one or more cameras, a first image of a region of the fish pen; processing the first image to determine that the first image includes a portion that represents a fish; identifying, within the portion, one or more markers associated with the fish; and generating a unique identification of the fish based at least on the one or markers.

11 . The method of claim 10 , further comprising:
computing, from the first image, one or more characteristics of the fish; and storing, in a database as a part of time series data, information representing the one or more characteristics computed from the first image in association with the unique identification of the fish.

12 . The method of claim 11 , further comprising:
obtaining at least a second image, wherein the second image is captured before or after the first image; detecting, within the second image, the one or more markers associated with the fish; computing, from the second image, the one or more characteristics of the fish; and storing, in the database as part of the time series data, information representing the one or more characteristics computed from the second image in association with the unique identification of the fish.

13 . The method of claim 11 , wherein the one or more characteristics include at least one of: a weight, a size, or a length of the fish.

14 . The method of claim 11 , wherein a region of the fish pen captured in the second image is different from the region of the fish pen captured in the first image.

15 . The method of claim 10 , wherein the one or more markers are derived based on a visual pattern present on the fish.

16 . The method of claim 10 , wherein the one or more markers are derived based on one of: a morphological mark or a genetic mark associated with the fish.

17 . The method of claim 10 , wherein the one or more markers are derived based on an electronic tag present on the fish, the electronic tag comprising one of: a microtag, a passive integrated transponder tag, a wire tag, or a radio tag.

18 . The method of claim 10 , wherein the portion that represents the fish is determined via object detection by a trained recurrent convolutional neural network (RCNN), or semantic segmentation that segments the portion from a background.

Assignees

• X Dev Llc

Inventors

• James Barnaby John
• Rapoport Evan Douglas
• Messana Matthew
• Kimball Peter

Classifications

• G06N3/09

  Supervised learning · CPC title

• G06N3/0464

  Convolutional networks [CNN, ConvNet] · CPC title

• G06V20/64

  Three-dimensional [3D] objects · CPC title

• G06T2207/20084

  Artificial neural networks [ANN] · CPC title

• A01K61/95

  specially adapted for fish · CPC title