Contemporary Amperex Technology Co., Ltd.

Contemporary Amperex Technology (hong Kong) Limited

Contemporary Amperex Technology Limited

Guannan Jiang

Zhiyu Wang

Fei Chen

Annan Shu

Qiangwei Huang

Jiwei Chen

Qian Wu

Lei Zhang

Chao Yuan

Xi Wang

Lin Ma

Lu Li

Boxiong Huang

Jinghua Huang

Lili Han

Jv Huang

Hongji Sun

Xiaoyi Chen

Tianhui Wu

Weilin Zhuang

Liangjin Hu

Kunming Zhang

Guan Chen

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.

Corner point detection method and apparatus

Embodiments of this application provide a method and apparatus for testing stability of a vision system, which can test whether the vision system is stable, so as to report a fault in a timely manner when the vision system is unstable, thereby guaranteeing accuracy of a measurement result of the vision system. The method includes: obtaining first position information of a spot inspection piece in a current image under the vision system; performing affine transformation on a reference contour model of the spot inspection piece for a position of the spot inspection piece in the current image based on the first position information, and obtaining, through fitting, a current contour model of the spot inspection piece in the current image based on the reference contour model; and determining stability of the vision system based on the reference contour model and the current contour model.

Method and apparatus for testing stability of vision system

The present application relates to a camera calibration method and apparatus, a computer device, a storage medium, and a program product. The method includes: obtaining a first operation instruction based on a camera calibration interface; determining, from configuration information, target configuration information matching the first operation instruction and set based on the camera calibration interface; and then executing a corresponding operation in a camera calibration process based on the first operation instruction and the target configuration information. However, the camera calibration process provided by embodiments of the present application is implemented based on the camera calibration interface, without switching back and forth between a plurality of tools, thereby making the camera calibration process less cumbersome.

Camera calibration method and apparatus, computer device, storage medium, and program product

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.

Method and apparatus for detecting defect on surface of cell

A correction method for a depth image of a battery includes obtaining the depth image of the battery. The depth image includes two adjacent surfaces of a shell assembly of the battery and an intersecting line between the two adjacent surfaces. The method further includes extracting the intersecting line from the depth image, determining planes in the depth image in which the two adjacent surfaces are located, and obtaining a datum plane based on the intersecting line and the planes in which the two adjacent surfaces are located. The datum plane is parallel to the intersecting line, and included angles between the datum plane and the planes in which the two adjacent surfaces are located are equal to each other. The method also includes projecting and mapping points in the depth image to the datum plane, to obtain depth information of points in a corrected depth image.

Correction method and apparatus for depth image, and method for welding inspection of shell assembly of battery

A calibration scale includes a scale body and at least two calibration pattern subsets arranged on the scale body along a first direction. The at least two calibration pattern subsets include a plurality of first calibration blocks arranged at spacings along the first direction and staggered along a second direction. Projections of the plurality of first calibration blocks in each calibration pattern subset along the first direction have a first overlapping area. Projections of at least two first overlapping areas along the first direction have a second overlapping area.

Calibration scale, calibration method and apparatus, and detection method and apparatus

Provided are a defect detection method and apparatus, and a computer-readable storage medium. Specifically, the method includes: obtaining a to-be-detected image; obtaining a feature map of the to-be-detected image based on the to-be-detected image, where the feature map of the to-be-detected image includes a feature map of spatial position coordinate information; and performing defect detection on the to-be-detected image based on the feature map of the to-be-detected image. By modifying a neural network structure of defect detection and extracting the feature map of spatial position coordinate information during the detection, this application makes the neural network for use of defect detection sensitive to a spatial position, thereby enhancing sensitivity of a detection neural network to the spatial position, and in turn, increasing accuracy of detecting some specific defect types by the detection neural network, and increasing accuracy of defect detection.

Defect detection method and apparatus, and computer-readable storage medium

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.

Debugging method and apparatus for production line devices and production line system

A welding mark inspection method includes acquiring a picture including a welding mark, and obtaining a classification type of the welding mark based on the picture and a welding mark classification model. The welding mark classification model is configured to classify the welding mark based on characteristics of the welding mark.

Welding mark inspection method and apparatus, and electronic device

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.

Apparatus and method for detecting tab folding, and image analyzer

A spot-checking method for a visual inspection system includes obtaining a plurality of to-be-inspected images, inspecting the plurality of to-be-inspected images to obtain defect types and/or parameters of a target object in the plurality of to-be-inspected images, and confirming availability of the visual inspection system based on the defect types and/or the parameters.

Method and apparatus for spot-checking visual inspection system

Embodiments of the present application provide a detection method, a detection device, and a storage medium. The detection method may comprise: obtaining a to-be-detected image; obtaining a plurality of confidence levels corresponding to a plurality of detection items according to the to-be-detected image; and determining a detection result of the to-be-detected image according to the plurality of confidence levels and a plurality of detection thresholds, where the plurality of detection thresholds may be corresponding to the plurality of detection items and may be a non-inferior solution of objective functions of an overkill rate and a missed detection rate, the overkill rate may be the ratio of qualified ones detected as defective, and the missed detection rate may be the ratio of defective ones detected as qualified.

Detection method, detection device and storage medium

A tab bending detection method and apparatus, an electronic device, and a storage medium are provided. The method includes: performing skeleton extraction on a sectional image of multiple layers of tabs to obtain a skeleton image of the multiple layers of tabs; merging damaged connected components in the skeleton image to obtain a merged connected component, where the damaged connected components are connected components on which breaking occurs in a same tab section; calculating a target number of the multiple layers of tabs based on the merged connected component and an undamaged connected component; and detecting, based on the target number and a preset number, whether any tab in the multiple layers of tabs is in a bending state. The damaged connected components are merged to obtain the merged connected component.

Tab bending detection method and apparatus, electronic device, and storage medium

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.

Defect detection method and apparatus

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.

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Ningde, China

Guannan Jiang