Abstract:
Spectral imaging systems are used to gather spectral image data on earthen material moving within an earthen material processing system, such as a mineral processing system or cement plant. Machine learning models such as 3D convolutional neural networks may be utilized to process the spectral image data to determine or classify one or more characteristics of the earthen material, such as ore grade, mineral alteration(s), moisture content, lithology and/or mineralogy. Such earthen material characteristics, or classifications thereof, may then be utilized to automatically control one or more operational characteristics of the earthen material processing system, such as rotational speed of milling equipment or flow rates of water or chemicals added to milling equipment or mineral concentration systems.
Abstract:
A method and apparatus for performing a fragmentation assessment of a material including fragmented material portions is disclosed. The method involves receiving two-dimensional image data representing a region of interest of the material, and processing the 2D image data to identify features of the fragmented material portions. The method also involves receiving a plurality of three dimensional point locations on surfaces of the fragmented material portions within the region of interest, identifying 3D point locations within the plurality of three dimensional point locations that correspond to identified features in the 2D image, and using the identified corresponding 3D point locations to determine dimensional attributes of the fragmented material portions.
Abstract:
A computer processor implemented method and system for monitoring a condition associated with operating heavy equipment is disclosed. The method involves receiving a plurality of images at an interface of an embedded processor disposed on the heavy equipment, the images providing a view of at least an operating implement of the heavy equipment. The method also involves processing each of the plurality of images using a first neural network implemented on the embedded processor, the first neural network having been previously trained to identify regions of interest within the image. Each region of interest has an associated designation as at least one of a critical region suitable for extraction of critical operating condition information required for operation of the heavy equipment, and a non-critical region suitable for extraction of non-critical operating condition information associated with the operation of the heavy equipment. The method further involves causing the embedded processor to initiate further processing of image data associated with critical regions to generate local output operable to alert an operator of the heavy equipment of the associated critical operating condition. The method also involves transmitting image data associated with non-critical regions to a remote processor, the remote processor being operably configured for further processing of the image data and to generate output signals representing results of the further processing. The method further involves receiving the output signals generated by the remote processor at one of the embedded processor or another processor associated with a heavy equipment operations worksite, the output signals being presentable via an electronic user interface based at least in part on the output signals to indicate the results of the further processing.
Abstract:
A method and apparatus for processing an image of fragmented material to identify fragmented material portions within the image is disclosed. The method involves receiving pixel data associated with an input plurality of pixels representing the image of the fragmented material. The method also involves processing the pixel data using a convolutional neural network, the convolutional neural network having a plurality of layers and producing a pixel classification output indicating whether pixels in the input plurality of pixels are located at one of an edge of a fragmented material portion, inwardly from the edge, and at interstices between fragmented material portions. The convolutional neural network includes at least one convolution layer configured to produce a convolution of the input plurality of pixels, the convolutional neural network having been previously trained using a plurality of training images including previously identified fragmented material portions. The method further involves processing the pixel classification output to associate identified edges with fragmented material portions.
Abstract:
An imaging system for earthen material, including a support structure adjacent an image location for a pathway of earthen material exposed to varying and uncontrolled illumination and/or artificial illumination, a spectral imager and a reference device each mounted to the support structure, the spectral imager directed at the image location and arranged to measure an intensity of illumination reflected from earthen material at the image location.
Abstract:
A method and apparatus for locating and/or determining the condition of a wear part in an image of an operating implement associated with heavy equipment is disclosed. The method involves capturing at least one image of the operating implement during operation of the heavy equipment, the image including a plurality of pixels each having an intensity value. The method also involves selecting successive pixel subsets within the plurality of pixels, and processing each pixel subset to determine whether pixel intensity values in the pixel subset meet a matching criterion indicating a likelihood that the pixel subset corresponds to the wear part. The matching criterion is based on processing a labeled set of training images during a training exercise prior to capturing the at least one image of the operating implement.
Abstract:
A method and apparatus for monitoring a condition of an operating implement in heavy equipment is disclosed. The method involves receiving a trigger signal indicating that the operating implement is within a field of view of an image sensor, and in response to receiving the trigger signal, causing the image sensor to capture at least one image of the operating implement. The method also involves processing the at least one image to determine the condition of the operating implement. A visual or audio warning or alarm may be generated for preventing significant damage to the processing equipment and avoid safety hazards involved.
Abstract:
A method and apparatus for locating and/or determining the condition of a wear part in an image of an operating implement associated with heavy equipment is disclosed. The method involves capturing at least one image of the operating implement during operation of the heavy equipment, the image including a plurality of pixels each having an intensity value. The method also involves selecting successive pixel subsets within the plurality of pixels, and processing each pixel subset to determine whether pixel intensity values in the pixel subset meet a matching criterion indicating a likelihood that the pixel subset corresponds to the wear part. The matching criterion is based on processing a labeled set of training images during a training exercise prior to capturing the at least one image of the operating implement.
Abstract:
A method and apparatus for processing an image of fragmented material to identify fragmented material portions within the image is disclosed. The method involves receiving pixel data associated with an input plurality of pixels representing the image of the fragmented material. The method also involves processing the pixel data using a convolutional neural network, the convolutional neural network having a plurality of layers and producing a pixel classification output indicating whether pixels in the input plurality of pixels are located at one of an edge of a fragmented material portion, inwardly from the edge, and at interstices between fragmented material portions. The convolutional neural network includes at least one convolution layer configured to produce a convolution of the input plurality of pixels, the convolutional neural network having been previously trained using a plurality of training images including previously identified fragmented material portions. The method further involves processing the pixel classification output to associate identified edges with fragmented material portions.
Abstract:
A method and apparatus for performing a fragmentation assessment of a material including fragmented material portions is disclosed. The method involves receiving two-dimensional image data representing a region of interest of the material, and processing the 2D image data to identify features of the fragmented material portions. The method also involves receiving a plurality of three dimensional point locations on surfaces of the fragmented material portions within the region of interest, identifying 3D point locations within the plurality of three dimensional point locations that correspond to identified features in the 2D image, and using the identified corresponding 3D point locations to determine dimensional attributes of the fragmented material portions.