Abstract: A method and apparatus for detecting a defect on a surface of a cell, including: obtaining an initial image of the surface of the cell by using an image acquisition unit; preprocessing the initial image to obtain at least one image to be detected of the surface of the cell; and inputting the at least one image to be detected into a defect detection neural network model, and obtaining a detection result outputted by the defect detection neural network model, the detection result being used to indicate whether there is a defect on the surface of the cell is described. According to technical solutions, features of the defect in the image may be made prominent, so as to facilitate subsequent detection of the defect detection neural network model; and more features may be extracted from the image, thereby improving the efficiency and accuracy of defect detection.

Abstract: Embodiments of this application provide a measurement method and a measurement apparatus. The measurement method includes: acquiring a first image and a second image of a target object, where the first image is acquired by a camera located on a non-backlight side of the target object, and the second image is acquired by a camera located on a backlight side of target object; and measuring the target object for size information according to the first image and the second image. The technical solution of this application can improve accuracy and precision of inspection while improving production efficiency.

Abstract: Disclosed, in one embodiment, are a method and device for detecting imaging consistency of a system, and a computer storage medium. The method includes: determining a target region in an image acquired by the system, where the target region is a partial region that includes a target object in the image acquired by the system; obtaining first image information of the target region; and detecting the imaging consistency of the system based on the first image information. By obtaining the first image information of the target region in the image acquired by the system and determining the imaging consistency of the system based on the first image information, this application can effectively detect the imaging consistency of the system and improve accuracy of product detection.

Abstract: A method and an apparatus for inspecting tab appearance of cell assembly, an electronic device, a non-transitory computer-readable storage medium, and a computer program product are provided. The method includes: obtaining an image for inspection that includes a background region and a cell assembly image region, where the cell assembly image region includes a body zone and a plurality of tab stacking regions, each tab stacking region adjoining a top edge or a bottom edge of the body zone; determining each root corner of the plurality of tab stacking regions in the image for inspection; determining two side edges of the body zone in the image for inspection; determining at least one reference edge line in the image for inspection based on the two side edges of the body zone in the image for inspection; and determining result information of the tab appearance inspection based on each root corner of the plurality of tab stacking regions in the image for inspection and the at least one reference edge line.

Abstract: A corner point detection method and apparatus, which may improve the efficiency of corner point detection is disclosed. The corner point detection method includes: obtaining a plurality of corner points of a target object in an image to be detected, where the plurality of corner points are corner points on a contour of a first object in the image to be detected, and the contour of the first object includes a contour of the target object; obtaining a region proportion corresponding to each of the plurality of corner points, where the region proportion is a proportion of a region of the first object in a predetermined region centered on the corner point; and determining a target corner point of the target object from the plurality of corner points based on the region proportion.

Abstract: A debugging method for production line devices may include: receiving control commands for a plurality of first devices in a production line, where respective control programs may be configured for the plurality of first devices; and calling the control programs for the plurality of first devices according to the control commands for the plurality of first devices respectively so as to debug the plurality of first devices.

Abstract: An image processing method includes searching for a next pixel of a skeleton branch of a target image based on an already-searched current pixel of the skeleton branch, and determining whether the skeleton branch is a burr branch based on a number of already-searched pixels of the skeleton branch.

Abstract: Embodiments of this application provide a defect detection method and apparatus. The method includes: obtaining an image for inspection; performing anomaly detection on the image for inspection to obtain an anomaly region image corresponding to the image for inspection; and performing defect classification on the anomaly region image to obtain defect detection information of the image for inspection. The defect detection method of the embodiments of this application is divided into two steps of anomaly detection and defect classification. Anomaly detection is performed on the image for inspection first, and then defect classification needs to be performed only on an anomaly region, reducing the workload of defect classification, thereby improving the efficiency of defect detection.

Abstract: An image recognition method may include: acquiring a target image, where the target image may include a weld bead region; performing initial segmentation on the target image, to obtain a first recognition result, where the first recognition result may include first recognition information for the weld bead region in the target image; performing feature extraction on the target image, to obtain a region representation; obtaining a context representation based on the first recognition result and the region representation, where the context representation may be used for representing a correlation between each pixel and remaining pixels in the target image; and obtaining a second recognition result based on the context representation, where the second recognition result may include second recognition information for the weld bead region in the target image.

Abstract: An image processing method includes obtaining two-dimensional and three-dimensional images of an object, extracting luminance and depth information from the three-dimensional image to generate luminance and depth image for the object, graying the two-dimensional image to obtain a single-channel grayscale image, selecting at least three same-location points from each of the grayscale and luminance image, calculating a coordinate transformation matrix between the grayscale and luminance images based on coordinates of the at least three same-location points in each of the grayscale and luminance images, and aligning the two-dimensional image with the luminance and depth images based on the coordinate transformation matrix to obtain fused image data. The fused image data includes color information of the two-dimensional image and the luminance and depth information of the three-dimensional image.

Abstract: The present application provides a method and a system for generating an image sample having a specific feature. The method includes: training a generative adversarial network-based sample generation model, where the generative adversarial network includes a generator and two discriminators: a global discriminator configured to perform global discrimination on an image, and a local discriminator configured to perform local discrimination on a specific feature; and inputting, to a trained generator that serves as a sample generation model, a semantic segmentation image that indicates a location of the specific feature and a corresponding real image not having the specific feature, to obtain a generated image sample having the specific feature.

Abstract: The present application provides a method and a system for generating an image sample having a specific feature. The method includes: training a generative adversarial network-based sample generation model, where the generative adversarial network includes a generator and two discriminators: a global discriminator configured to perform global discrimination on an image, and a local discriminator configured to perform local discrimination on a specific feature; and inputting, to a trained generator that serves as a sample generation model, a semantic segmentation image that indicates a location of the specific feature and a corresponding real image not having the specific feature, to obtain a generated image sample having the specific feature.

Abstract: A method for training a cell defect detection model includes training a defect classification model that includes an output layer using a plurality of first sample images so that a defect classification model obtained through training is capable of predicting a plurality of first-preset-category defects of a cell, inputting a second sample image to a defect classification model with at least an output layer removed to obtain a sample feature vector of the second sample image, inputting the sample feature vector of the second sample image to a backbone model to obtain a predicted defect classification result of the second sample image, and adjusting, based on a second-preset-category defect and the predicted defect classification result of the second sample image, parameters of the backbone model and the defect classification model with at least the output layer removed.

Abstract: An image processing method includes obtaining two-dimensional and three-dimensional images of an object, extracting luminance and depth information from the three-dimensional image to generate luminance and depth image for the object, graying the two-dimensional image to obtain a single-channel grayscale image, selecting at least three same-location points from each of the grayscale and luminance image, calculating a coordinate transformation matrix between the grayscale and luminance images based on coordinates of the at least three same-location points in each of the grayscale and luminance images, and aligning the two-dimensional image with the luminance and depth images based on the coordinate transformation matrix to obtain fused image data. The fused image data includes color information of the two-dimensional image and the luminance and depth information of the three-dimensional image.

Abstract: A data annotation method for image segmentation includes performing training using an annotated training sample set to obtain a data annotation model, inputting an original image sample to the data annotation model to obtain an automatic annotation result corresponding to the original image sample, visually presenting the automatic annotation result together with the original image sample based on a preset annotation strategy, receiving correction to the automatic annotation result and converting the received correction into a corrected annotation result based on the annotation strategy, and optimizing the data annotation model using the corrected annotation result and the original image sample together as a new annotated training sample.

Abstract: An image recognition method may include: acquiring a target image, where the target image may include a weld bead region; performing initial segmentation on the target image, to obtain a first recognition result, where the first recognition result may include first recognition information for the weld bead region in the target image; performing feature extraction on the target image, to obtain a region representation; obtaining a context representation based on the first recognition result and the region representation, where the context representation may be used for representing a correlation between each pixel and remaining pixels in the target image; and obtaining a second recognition result based on the context representation, where the second recognition result may include second recognition information for the weld bead region in the target image.

Abstract: This application provides an apparatus and method for detecting tab folds, and an image analyzer. The apparatus for detecting tab folds includes: a first image obtaining module, configured to obtain a first image of a first lateral face of tabs of a battery cell; a second image obtaining module, configured to obtain a second image of a second lateral face of the tabs, where the second lateral face is different from the first lateral face; and an image analyzer, configured to obtain, based on the first image, a first number of layers of the tabs corresponding to the first lateral face, and obtain, based on the second image, a second number of layers of the tabs corresponding to the second lateral face, and determine, based on at least one of the first number of layers or the second number of layers, whether the tabs are in a folded state.