A material handling system sorts materials utilizing a vision system of multiple vision devices configured with single board computers that each implement an artificial intelligence system in order to identify or classify materials, which are then sorted into separate groups based on such an identification or classification by sorting devices that are each coupled to one of the vision devices.

A process includes: (i) receiving image data, dividing a portion of the image data into a plurality of grids including a first grid, a second grid, a third grid, and a fourth grid, dividing the portion of the image data into a plurality of areas including (a) a first area including the first grid and the third grid, and (b) a second area including the second grid and the fourth grid; (ii) detecting an object relating to the first grid; and (iii) when the object is detected in the first grid, generating a signal for controlling discharge of fluid from a fluid discharging unit so that fluid flows through the first area including the first grid.

A method, system and platform for implementing an automated grading system that can reliably and efficiently grade a trading object such as numismatics and trading cards are disclosed. Via adopting industry-standard grading scales, the present computer-assisted numismatics and sports trading card platform utilizes various techniques, such as laser scanning, machine vision, smartphone IOS and Android native installed object recognition, neural network models, blockchain, NFT with smart contracts, digital fingerprints identified as intellectual property and royalties to enable consistent grading and trading of a large quantity of trading objects. It can further enable authenticity verification, and transactions of the graded trading objects.

A weld-line generating apparatus includes a point-cloud-data acquiring unit that acquires 3D point cloud data of workpieces to be welded that are arranged in a predetermined space, an edge extracting unit that extracts 3D point cloud data of edges from the 3D point cloud data acquired by the point-cloud-data acquiring unit, a workpiece point-cloud-data generating unit that generates a 3D point cloud data component of each of the workpiece based on 3D point cloud data that is obtained by removing the 3D point cloud data of edges extracted by the edge extracting unit from the 3D point cloud data acquired by the point-cloud-data acquiring unit, and a weld-line generating unit 24 that generates weld lines for the workpieces based on the 3D point cloud data components of the workpieces generated by the workpiece point-cloud-data generating unit.

Drone-based inventory management method and systems. One embodiment provides a drone-based inventory management system including one or more unmanned aerial vehicles (UAVs), and a central management system having an electronic processor, and a transceiver configured to communicate with the one or more UAVs. The electronic processor is configured to determine a discrepancy in inventory and select a UAV for verification. The electronic processor is also configured to determine whether weather permits UAV operation and operate the UAV in a pre-determined route when the weather permits UAV operation. The electronic processor is further configured to capture images using the UAV and determine new inventory based on captured images. The electronic processor is also configured to update inventory based on the new inventory.

The present invention relates to a method for detecting changes in the ore grade of a rock face in near real time. The method includes the step of providing a scanning system having at least a hyperspectral imager, a position system, a LiDAR or range determination unit and computational resources. Further, the method involves determining a precise location of the scanning system utilising the position system. The rock face is scanned with the range determination unit to determine rock face position information. The method involves scanning the rock face with the hyperspectral imager to produce a corresponding rock face hyperspectral image. Further the method involves utilising the computational resources to fuse together the rock face position information and the corresponding rock face hyperspectral image to produce a rock face position and content information map of the rock face.

An inspection apparatus includes an image-capturing unit configured to capture an image of an article, an inspection unit configured to inspect the article, a generation-processing unit, and a storage-processing unit. The generation-processing unit generates a data group related to an inspection result obtained by the inspection unit, the data group including article information for distinguishing the article, the inspection result obtained by the inspection unit, captured-image information for distinguishing a captured image captured by the image-capturing unit from another captured image, and a hash value of the captured image that are associated with each other. The storage-processing unit stores the data group and the captured image in respective different storage units.

Systems and methods are disclosed for detecting defects during manufacturing processes for materials and products. The provided systems may utilize imaging units and computer detection algorithms to determine the presence or absence of defects in manufactured materials or products. Detection of defects in material or products by the disclosed systems may prompt intervention in the manufacturing process to correct the source of the defects.

A method for detecting defects in a formed metal part includes locating one or more regions of interest in a synthetic image of a part manufactured by a forming process. The synthetic image is informed based on a physics-based simulation of the forming process. The regions of interest indicate a high risk of having a defect from the forming process. A set of training images including real images of actual manufactured parts are registered with the synthetic image. The regions of interest are overlaid on each training image, to extract patches from the training images that correspond to high-risk regions. An anomaly detection model is trained on the patches extracted from the training images to detect a defect in a formed metal part from an acquired image of the formed metal part, by detecting an anomaly in a patch extracted from the acquired image that corresponds to a high-risk region.

A system for performing computer-assisted image analysis of welds and related methods is disclosed. Digital images are captured at a worksite and sent to a remote image analysis system of a weld analytics system that analyzes images to determine whether the images conform to weld specifications. The remote image analysis system may be trained by artificial intelligence or machine learning.