X Development LLC

Tidalx Ai Inc.

Matthew Messana

Barnaby John James

Peter Kimball

Neil Davé

Shane Washburn

Kyle James Cormany

Joel Fraser Atwater

Christopher Thornton

Grace Calvert Young

Evan Douglas Rapoport

Jacques L Gagne

Matthew David Day

Ferdinand Legros

James Daniel Haley

Matthew Aaron Knoll

Stephen Duane Dorow

Samuel Meyer Cohen

Neil DavÉ

A sensor positioning system, includes an actuation server for communicating with components of the sensor positioning system. The sensor positioning system additionally includes a first actuation system and a second actuation system, wherein each actuation system includes a pulley system for maneuvering an underwater sensor system. The sensor positioning system includes a dual point attachment bracket that connects through a first line to the first actuation system and connecting through a second line to the second actuation system. The underwater sensor system is affixed to the first pulley system, the second pulley system, and the dual attachment bracket through the first line and the second line.

Sensor positioning system

Methods, systems, and apparatuses, including computer programs encoded on a computer-readable storage medium for estimating the shape, size, mass, and health of fish are described. A pair of stereo cameras may be utilized to obtain off-axis images of fish in a defined area. The 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 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 fish model to determine characteristics of the fish.

Fish biomass, shape, size, or health determination

A fish monitoring system deployed in a particular area to obtain fish images is described. Neural networks and machine-learning techniques may be implemented to periodically train fish monitoring systems and generate monitoring modes to capture high quality images of fish based on the conditions in the determined area. The camera systems may be configured according to the settings, e.g., positions, viewing angles, specified by the monitoring modes when conditions matching the monitoring modes are detected. Each monitoring mode may be associated with one or more fish activities, such as sleeping, eating, swimming alone, and one or more parameters, such as time, location, and fish type.

Fish measurement station keeping

Methods, systems, and apparatus, including computer programs encoded on computer-storage media, for a lighting controller for sea lice detection. In some implementations, fish are contained within an elliptical tank filled with water. An imaging station located on the elliptical tank is used to capture an image of a fish from which image analysis can be performed to detect sea lice or other skin features, including lesions, on the fish. Pairs of imaging assemblies coordinate pulsing light of at least a first and a second color and capturing images of the fish while the fish is illuminated by at least the first and the second color. By using the captured images with different color light, computers can detect features on the body of a fish including sea lice, skin lesions, shortened operculum or other physical deformities and skin features. Detection results can aid in mitigation techniques or be stored for analytics. For example, sea lice detection results can inform targeted treatments comprised of lasers, fluids, or mechanical devices such as a brush or suction.

Multi-chamber lighting controller for aquaculture

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.

Fish biomass, shape, and size determination

Methods, systems, and apparatus, including computer programs encoded on computer-storage media, for a lighting controller for sea lice detection. In some implementations, a pulse of red light and a pulse of blue light can be timed with the exposure of a camera to capture multiple images of a fish or group of fishes in both red and blue light. By using the captured images with different color light, computers can detect features on the body of a fish including sea lice, skin lesions, shortened operculum or other physical deformities and skin features. Detection results can aid in mitigation techniques or be stored for analytics. For example, sea lice detection results can inform targeted treatments comprised of lasers, fluids, or mechanical devices such as a brush or suction.

Lighting controller for sea lice detection

Unit controller

Underwater camera

Growing community of inventors

Sunnyvale, CA, United States of America

Matthew Messana