Abstract: An example wear detection system receives first image data related to at least one ground engaging tool (GET) of a work machine from one or more sensors at a first time instance in a dig-dump cycle of the work machine. The wear detection system processes the first image data to determine a first wear measurement and first wear level for the at least one GET. The wear detection system determines whether the first wear level is indicative of a GET replacement condition. The wear detection system generates an alert when the first wear level is indicative of the GET replacement condition. The wear detection system receives second image data related to the at least one GET a second time instance different from the first time instance when the first wear level is not indicative of the GET replacement condition and determines a second wear measurement and second wear level for the at least one GET.

Abstract: An example wear detection system receives a left image and a right image of a bucket of a work machine having at least one ground engaging tool (GET). The example system identifies a first region of interest from the left image corresponding to the GET and a second region of interest from the right image corresponding to the GET. The example system also generates a left-edge digital image corresponding to the first region of interest and a right-edge digital image corresponding to the second region of interest. Further, the example system determines a sparse stereo disparity between the left-edge digital image and the right-edge digital image, and also determines a wear level or loss for the at least one GET based on the sparse stereo disparity.

Abstract: An example wear detection system receives first imaging data from one or more sensors associated with a work machine. The first imaging data comprises data related to at least one ground engaging tool (GET) of the work machine. The example system identifies a region of interest including data of the at least one GET within the first imaging data. Based on the identified region of interest, the example system controls a LiDAR sensor to capture second imaging data capturing the at least one GET that is of higher resolution than the first imaging data. The example system generates a three-dimensional point cloud of the at least one GET based on the second imaging data and determines a wear level or loss for the at least one GET based on the three-dimensional point cloud.

Abstract: An example wear detection system receives a plurality of images from a plurality of sensors associated with a work machine. Individual sensors of the plurality of sensors have respective fields-of-view different from other sensors of the plurality of sensors. The wear detection system identifies a first region of interest and second region of interest associated with the at least one GET. The wear detection system determines a first set of image points and a second set of images points for the at least one GET based on geometric parameters associated with the GET. The wear detection system determines a wear level or loss for the at least one GET based on the GET measurement.

Abstract: An example wear detection system receives first image data related to at least one ground engaging tool (GET) of a work machine from one or more sensors at a first time instance in a dig-dump cycle of the work machine. The wear detection system processes the first image data to determine a first wear measurement and first wear level for the at least one GET. The wear detection system determines whether the first wear level is indicative of a GET replacement condition. The wear detection system generates an alert when the first wear level is indicative of the GET replacement condition. The wear detection system receives second image data related to the at least one GET a second time instance different from the first time instance when the first wear level is not indicative of the GET replacement condition and determines a second wear measurement and second wear level for the at least one GET.

Abstract: Changes in ambient light intensity are detected by processing image data from an image capture device to determine, iteratively, a signal-to-noise ratio of the image data. The signal-to-noise ratio is a ratio of average to variance of pixel values for some of the pixels forming the image. A control outputis generated, based on the signal-to-noise ratio, that is responsive to changes in ambient light intensity. The control output is used control a light source of a vehicle.

Abstract: An example wear detection system receives first imaging data from one or more sensors associated with a work machine. The first imaging data comprises data related to at least one ground engaging tool (GET) of the work machine. The example system identifies a region of interest including data of the at least one GET within the first imaging data. Based on the identified region of interest, the example system controls a LiDAR sensor to capture second imaging data capturing the at least one GET that is of higher resolution than the first imaging data. The example system generates a three-dimensional point cloud of the at least one GET based on the second imaging data and determines a wear level or loss for the at least one GET based on the three-dimensional point cloud.

Abstract: An example wear detection system receives a left image and a right image of a bucket of a work machine having at least one ground engaging tool (GET). The example system identifies a first region of interest from the left image corresponding to the GET and a second region of interest from the right image corresponding to the GET. The example system also generates a left-edge digital image corresponding to the first region of interest and a right-edge digital image corresponding to the second region of interest. Further, the example system determines a sparse stereo disparity between the left-edge digital image and the right-edge digital image, and also determines a wear level or loss for the at least one GET based on the sparse stereo disparity.

Abstract: A system and method for dust mitigation in an environment with high amounts of debris is disclosed. The system may include an optical device disposed within an interior volume of an enclosure. A positive flow of clean air may be maintained into an air input aperture of the enclosure, around the optical device, over a lens of the optical device, and out a lens aperture of the enclosure. The system may be affixed to a piece of equipment, machine, or similar structure via a positionable or pivotable mount.