FISH MEASUREMENT STATION KEEPING
    21.
    发明申请

    公开(公告)号:US20190340440A1

    公开(公告)日:2019-11-07

    申请号:US15970131

    申请日:2018-05-03

    Abstract: 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.

    LIGHTING CONTROLLER FOR SEA LICE DETECTION

    公开(公告)号:US20220201987A1

    公开(公告)日:2022-06-30

    申请号:US17697388

    申请日:2022-03-17

    Abstract: 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.

    FISH BIOMASS, SHAPE, AND SIZE DETERMINATION

    公开(公告)号:US20220012479A1

    公开(公告)日:2022-01-13

    申请号:US17474893

    申请日:2021-09-14

    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.

    Fish measurement station keeping
    27.
    发明授权

    公开(公告)号:US11113539B2

    公开(公告)日:2021-09-07

    申请号:US16701853

    申请日:2019-12-03

    Abstract: 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 biomass, shape, and size determination

    公开(公告)号:US10599922B2

    公开(公告)日:2020-03-24

    申请号:US15879851

    申请日:2018-01-25

    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.

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