Escape detection and mitigation for aquaculture
Methods, systems, and apparatus, including computer programs encoded on computer-storage media, for escape detection and mitigation for aquaculture. In some implementations, a method includes obtaining one or more images that depict one or more fish within a population of fish that are located within an enclosure; providing, to one or more detection models configured to classify fish that are depicted within the images as likely being member or as likely not being member of a type of fish, the one or images; generating, as a result of providing the one or more images to the one or more detection models, a value that reflects a quantity of fish that are depicted in the images that are likely a member of the type of fish; and detecting a condition based at least on the value.
1. A computer-implemented method comprising:
obtaining one or more images that depict one or more fish within a population of fish that are located within an enclosure;
providing, to one or more detection models configured to classify fish that are depicted within the images as likely being member or as likely not being member of a type of fish, the one or images;
generating, as a result of providing the one or more images to the one or more detection models, a value that reflects a quantity of fish that are depicted in the images that are likely a member of the type of fish; and
detecting a possible opening of the enclosure based at least on the value.
2. The method of claim 1 , comprising:
determining a location of the quantity of fish within the enclosure.
3. The method of claim 2 , comprising:
obtaining location data from a device that captures the one or more images; and
determining the location of the quantity of fish within the enclosure using the location data.
4. The method of claim 1 , wherein detecting the possible opening of the enclosure based at least on the value comprises:
detecting a condition based at least on the value.
5. The method of claim 1 , wherein the value represents a ratio indicating a number of fish of the type of fish.
6. The method of claim 1 , wherein the type of fish is a population raised for aquaculture in the enclosure.
7. The method of claim 1 , comprising:
sending a signal to a controllable device configured to move towards a location specified in the signal relative to the enclosure.
8. The method of claim 1 , comprising:
storing locations corresponding to locations of the one or more fish enclosed within the enclosure.
9. The method of claim 1 , wherein the one or more detection models comprise a machine learning model trained using images of the type of fish.
10. A non-transitory, computer-readable medium storing one or more instructions executable by a computer system to perform operations comprising:
obtaining one or more images that depict one or more fish within a population of fish that are located within an enclosure;
providing, to one or more detection models configured to classify fish that are depicted within the images as likely being member or as likely not being member of a type of fish, the one or images;
generating, as a result of providing the one or more images to the one or more detection models, a value that reflects a quantity of fish that are depicted in the images that are likely a member of the type of fish; and
detecting a possible opening of the enclosure based at least on the value.
11. The medium of claim 10 , wherein the operations comprise:
determining a location of the quantity of fish within the enclosure.
12. The medium of claim 11 , wherein the operations comprise:
obtaining location data from a device that captures the one or more images; and
determining the location of the quantity of fish within the enclosure using the location data.
13. The medium of claim 10 , wherein detecting the possible opening of the enclosure based at least on the value comprises:
detecting a condition based at least on the value.
14. The medium of claim 10 , wherein the value represents a ratio indicating a number of fish of the type of fish.
15. The medium of claim 10 , wherein the type of fish is a population raised for aquaculture in the enclosure.
16. The medium of claim 10 , wherein the operations comprise:
sending a signal to a controllable device configured to move towards a location specified in the signal relative to the enclosure.
17. The medium of claim 10 , wherein the operations comprise:
storing locations corresponding to locations of the one or more fish enclosed within the enclosure.
18. The medium of claim 10 , wherein the one or more detection models comprise a machine learning model trained using images of the type of fish.
19. 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 one or more images that depict one or more fish within a population of fish that are located within an enclosure;
providing, to one or more detection models configured to classify fish that are depicted within the images as likely being member or as likely not being member of a type of fish, the one or images;
generating, as a result of providing the one or more images to the one or more detection models, a value that reflects a quantity of fish that are depicted in the images that are likely a member of the type of fish; and
detecting a possible opening of the enclosure based at least on the value.
20. The system of claim 19 , wherein the operations comprise:
determining a location of the quantity of fish within the enclosure.