A safety belt detection method, apparatus, computer device and computer readable storage medium are disclosed. The safety belt detection method includes the steps as follows. An image to be detected is obtained. The image to be detected is inputted into a detection network which includes a global dichotomous branch network and a grid classification branch network. A dichotomous result, which indicates whether a driver is wearing a safety belt and is output from the global dichotomous branch network, is obtained. A grid classification diagram, which indicates a position information of the safety belt and is output from the grid classification branch network, is obtained based on image classification. A detection result of the safety belt, indicating whether the driver is wearing the safety belt normatively, is obtained based on the dichotomous result and the grid classification diagram.

Provided is image processing unit that causes computer to perform: a spatter candidate region detection step of performing, for each of input images obtained by capturing workpiece during arc welding, detection of a spatter candidate region based on a pixel value indicating brightness of a pixel included in the input images; a reflected light region identification step of identifying, in the spatter candidate region detected in the spatter candidate region detection step, a reflected light region in which reflected light of arc light is shown, based on color information of a reference pixel included in the spatter candidate region; and a spatter number identification step of identifying, as the number of spatters, the number of spatter candidate regions obtained by removing the reflected light region identified in the reflected light region identification step in the spatter candidate region detected in the spatter candidate region detection step.

Systems and methods for vision test and uses thereof are disclosed. A method may be implemented on a mobile device having at least a processor, a camera and a display screen. The method may include capturing at least one image of a user using the camera of the mobile device; interactively guiding the user to a predetermined distance from the display screen of the mobile device based on the at least one image; presenting material on the display screen upon a determination that the user is at the predetermined distance from the display screen; and receiving input from the user in response to the material presented on the display screen. The material presented on the display screen may be for assessing at least one characteristic of the user's vision. Mobile devices and non-transitory machine-readable mediums having machine-executable instructions embodied thereon for assessing a user's vision also are disclosed.

Systems and methods for vision test and uses thereof are disclosed. A method may be implemented on a mobile device having at least a processor, a camera and a display screen. The method may include capturing at least one image of a user using the camera of the mobile device; interactively guiding the user to a predetermined distance from the display screen of the mobile device based on the at least one image; presenting material on the display screen upon a determination that the user is at the predetermined distance from the display screen; and receiving input from the user in response to the material presented on the display screen. The material presented on the display screen may be for assessing at least one characteristic of the user's vision. Mobile devices and non-transitory machine-readable mediums having machine-executable instructions embodied thereon for assessing a user's vision also are disclosed.

The current disclosure provides methods and systems for visualizing, comparing, and navigating through, labeled image sequences. In one example, a degree of variation between a plurality of labels for an image in a sequence of images may be encoded as a comparison metric, and the comparison metric for each image may be graphed as a function of image position in the sequence of images, thereby providing a contextually rich view of label variation as a function of progression through the sequence of images. Further, the encoded variation of image labels may be used to automatically flag inconsistently labeled images, wherein the flagged images may be highlighted in a graphical user interface presented to a user, pruned from a training dataset, or a loss associated with the flagged image may be scaled based on the encoded variation during training of a machine learning model.

The current disclosure provides methods and systems for visualizing, comparing, and navigating through, labeled image sequences. In one example, a degree of variation between a plurality of labels for an image in a sequence of images may be encoded as a comparison metric, and the comparison metric for each image may be graphed as a function of image position in the sequence of images, thereby providing a contextually rich view of label variation as a function of progression through the sequence of images. Further, the encoded variation of image labels may be used to automatically flag inconsistently labeled images, wherein the flagged images may be highlighted in a graphical user interface presented to a user, pruned from a training dataset, or a loss associated with the flagged image may be scaled based on the encoded variation during training of a machine learning model.

Various examples include systems, apparatuses, and methods to perform an automated visual-inspection of components undergoing various stages of fabrication. In one example, an inspection system includes a number of robots, each having a camera, to inspect a component for defects at various stages of fabrication. Generally, each of the cameras is located at a different geographical location corresponding to the various stages in the fabrication of the component. At least some of the cameras are arranged to inspect all surfaces of the component that are not facing a table upon which the component is mounted. The system also includes a respective data-collection station electronically coupled to each the number of robots and an associated one of the cameras. A master data-collection station is electronically coupled to each of the data-collection stations. Other systems, apparatuses, and methods are disclosed.

Various examples include systems, apparatuses, and methods to perform an automated visual-inspection of components undergoing various stages of fabrication. In one example, an inspection system includes a number of robots, each having a camera, to inspect a component for defects at various stages of fabrication. Generally, each of the cameras is located at a different geographical location corresponding to the various stages in the fabrication of the component. At least some of the cameras are arranged to inspect all surfaces of the component that are not facing a table upon which the component is mounted. The system also includes a respective data-collection station electronically coupled to each the number of robots and an associated one of the cameras. A master data-collection station is electronically coupled to each of the data-collection stations. Other systems, apparatuses, and methods are disclosed.

The present invention provides a processing apparatus (10) including: an object region detection unit (11) that detects, from an image, an object region being a region including an object; a reliability computation unit (12) that computes, for each product, reliability in which each of the products is included in an image of the object region; an image parameter computation unit (13) that computes an image parameter related to an image of the object region; a threshold value setting unit (14) that sets a threshold value of the reliability, based on the image parameter; and a product determination unit (15) that determines, based on the reliability of each of the products and the threshold value, the product included in an image of the object region.

The present invention provides a processing apparatus (10) including: an object region detection unit (11) that detects, from an image, an object region being a region including an object; a reliability computation unit (12) that computes, for each product, reliability in which each of the products is included in an image of the object region; an image parameter computation unit (13) that computes an image parameter related to an image of the object region; a threshold value setting unit (14) that sets a threshold value of the reliability, based on the image parameter; and a product determination unit (15) that determines, based on the reliability of each of the products and the threshold value, the product included in an image of the object region.