Abstract: The present disclosure provides a dimensional measurement method and device based on deep learning. The method includes capturing a target image of a target object, obtaining measurement data for the target image, and determining whether or not the target object is within a preset tolerance.

Abstract: The present disclosure provides a dimensional measurement method and device based on deep learning. The method includes capturing a target image of a target object, obtaining measurement data for the target image, and determining whether or not the target object is within a preset tolerance.

Abstract: A fused imaging device and method are disclosed. The device comprises a light source component, an image capture component, and a control component. The control component may be configured to control the light source component to illuminate a target object based on a preset plurality of first optimal lighting configurations. The control component may further control the image capture component to capture images of the target object to obtain multiple first images, under the illumination of the light source component and generate a target image of the target object, based on the first images. The control component may further adjust the incidence angle, pattern, and wavelength of the light source in the light source component, as well as the exposure, lens focus, and polarization of the image capture component, to detect target object defects in captured images of the target object.

Abstract: A fused imaging device and method are disclosed. The device comprises a light source component, an image capture component, and a control component. The control component may be configured to control the light source component to illuminate a target object based on a preset plurality of first optimal lighting configurations. The control component may further control the image capture component to capture images of the target object to obtain multiple first images, under the illumination of the light source component and generate a target image of the target object, based on the first images. The control component may further adjust the incidence angle, pattern, and wavelength of the light source in the light source component, as well as the exposure, lens focus, and polarization of the image capture component, to detect target object defects in captured images of the target object.

Abstract: The present disclosure provides a dimensional measurement method and device based on deep learning. The method includes capturing a target image of a target object, obtaining measurement data for the target image, and determining whether or not the target object is within a preset tolerance.

Abstract: A fused imaging device and method are disclosed. The device comprises a light source component, an image capture component, and a control component. The control component may be configured to control the light source component to illuminate a target object based on a preset plurality of first optimal lighting configurations. The control component may further control the image capture component to capture images of the target object to obtain multiple first images, under the illumination of the light source component and generate a target image of the target object, based on the first images. The control component may further adjust the incidence angle, pattern, and wavelength of the light source in the light source component, as well as the exposure, lens focus, and polarization of the image capture component, to detect target object defects in captured images of the target object.

Abstract: A fused imaging device and method are disclosed. The device comprises a light source component, an image capture component, and a control component. The control component may be configured to control the light source component to illuminate a target object based on a preset plurality of first optimal lighting configurations. The control component may further control the image capture component to capture images of the target object to obtain multiple first images, under the illumination of the light source component and generate a target image of the target object, based on the first images. The control component may further adjust the incidence angle, pattern, and wavelength of the light source in the light source component, as well as the exposure, lens focus, and polarization of the image capture component, to detect target object defects in captured images of the target object.

Abstract: The present disclosure relates to a method, devices, and storage medium for training neural networks based on memory scores. The said method comprises: establishing the memory scores of a plurality of first-sample images in the library, from their training ages and training indicators, and a preset discount rate; determining a plurality of second-sample images from these memory scores and a preset first count, and using them to establish the first training set; training the neural network by using the first training set, with the said neural network is used for defect detection. The neural network training method in the disclosed embodiment reduces the size of the training set and shortens the time to converge, thereby improving training efficiency.

Abstract: A defect detection device includes an image capturing component for capturing one or more images of an object to be inspected; a motion component configured to grasp or manipulate the object or the image capturing component; and a computing device configured to perform a defect detection method, including determining a plurality of first image capturing poses of the object to be inspected; determining a first defect probability for each particular first image capturing pose; establishing a probability matrix based on the first defect probabilities; subdividing the probability matrix into a plurality of submatrices according to preset dimensions for each submatrix; determining a second defect probability for each of the plurality of submatrices; setting a maximum value of the second defect probabilities as a third defect probability of the object to be inspected; and comparing the third defect probability to a threshold to determine whether the object is defective.