Granted Patent B2

US 12,254,712 · App. 17/716,797 · Granted Mar 18, 2025

Inventors: Julia Black Ling (Redwood City, CA); Laura Valentine Chrobak (Menlo Park, CA)

Assignee: TidaIX AI Inc.

G06V40/10G06T7/62G06Q50/02

Abstract

Claims (58)

1. A method comprising:

obtaining an image of a fish captured by a monocular underwater camera;

providing the image of the fish to a depth perception model;

generating, by the depth perception model, an output including a depth-enhanced image of the fish;

determining a biomass estimate of the fish based on the output depth-enhanced image of the fish; and

performing an action based on one or more biomass estimates including the biomass estimate of the fish.

2. The method of claim 1 , comprising:

determining, based on the depth-enhanced image of the fish, a data set including a value that indicates a length between a first point on the fish and a second point on the fish.

3. The method of claim 2 , wherein determining the biomass value of the fish comprises:

providing the data set including the value that indicates the length between the first point on the fish and the second point on the fish to a model trained to predict biomass; and

generating, by the model trained to predict biomass, an output including the biomass value of the fish.

4. The method of claim 2 , comprising:

detecting the first point and the second point on the fish.

5. The method of claim 4 , wherein detecting the first and second points comprise:

providing the depth-enhanced image of the fish to a model trained to detect feature points on a fish body.

6. The method of claim 1 , comprising:

detecting the fish within the image using a model trained to detect fish.

7. The method of claim 1 , wherein the action comprises:

adjusting a feeding system providing feed to the fish.

8. The method of claim 1 , wherein the action comprises:

sending data including the biomass estimate to a user device, wherein the data is configured to, when displayed on the user device, present a user of the user device with a visual representation of the biomass estimate.

9. The method of claim 1 , comprising:

obtaining data from the monocular underwater camera indicating a current operation status of the monocular underwater camera; and

in response to obtaining the image of the fish and the data from the monocular underwater camera, providing the image of the fish to the depth perception model.

10. The method of claim 1 , wherein determining the action comprises:

determining to adjust a position or operation of an item of motorized equipment.

11. A non-transitory, computer-readable medium storing one or more instructions executable by a computer system to perform operations comprising:

obtaining an image of a fish captured by a monocular underwater camera;

providing the image of the fish to a depth perception model;

generating, by the depth perception model, an output including a depth-enhanced image of the fish;

determining a biomass estimate of the fish based on the output depth-enhanced image of the fish; and

performing an action based on one or more biomass estimates including the biomass estimate of the fish.

12. The non-transitory, computer-readable medium of claim 11 , comprising:

determining, based on the depth-enhanced image of the fish, a data set including a value that indicates a length between a first point on the fish and a second point on the fish.

13. The non-transitory, computer-readable medium of claim 12 , wherein determining the biomass value of the fish comprises:

providing the data set including the value that indicates the length between the first point on the fish and the second point on the fish to a model trained to predict biomass; and

generating, by the model trained to predict biomass, an output including the biomass value of the fish.

14. The non-transitory, computer-readable medium of claim 12 , comprising:

detecting the first point and the second point on the fish.

15. The non-transitory, computer-readable medium of claim 14 , wherein detecting the first and second points comprise:

providing the depth-enhanced image of the fish to a model trained to detect feature points on a fish body.

16. The non-transitory, computer-readable medium of claim 11 , comprising:

detecting the fish within the image using a model trained to detect fish.

17. The non-transitory, computer-readable medium of claim 11 , wherein the action comprises:

adjusting a feeding system providing feed to the fish.

18. The non-transitory, computer-readable medium of claim 11 , wherein the action comprises:

sending data including the biomass estimate to a user device, wherein the data is configured to, when displayed on the user device, present a user of the user device with a visual representation of the biomass estimate.

19. The non-transitory, computer-readable medium of claim 11 , comprising:

obtaining data from the monocular underwater camera indicating a current operation status of the monocular underwater camera; and

in response to obtaining the image of the fish and the data from the monocular underwater camera, providing the image of the fish to the depth perception model.

20. A computer-implemented system, comprising:

one or more computers; and

one or more computer memory devices interoperably coupled with the one or more computers and having tangible, non-transitory, machine-readable media storing one or more instructions that, when executed by the one or more computers, perform one or more operations comprising:

obtaining an image of a fish captured by a monocular underwater camera;

providing the image of the fish to a depth perception model;

generating, by of the depth perception model, an output including a depth-enhanced image of the fish;

determining a biomass estimate of the fish based on the output depth-enhanced image of the fish; and

performing an action based on one or more biomass estimates including the biomass estimate of the fish.

Assignments (2)

Continuity (1)

References Cited (31)