CN117409006B - Bubble contour detection method, film repair method, electronic device and storage medium - Google Patents

Bubble contour detection method, film repair method, electronic device and storage medium Download PDF

Info

Publication number
CN117409006B
CN117409006B CN202311722575.XA CN202311722575A CN117409006B CN 117409006 B CN117409006 B CN 117409006B CN 202311722575 A CN202311722575 A CN 202311722575A CN 117409006 B CN117409006 B CN 117409006B
Authority
CN
China
Prior art keywords
thermal image
bubble
target
determining
target thermal
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Active
Application number
CN202311722575.XA
Other languages
Chinese (zh)
Other versions
CN117409006A (en
Inventor
冯钧
韦承利
陈飞
刘银超
陈亚楠
陈立
陈熙
张宇航
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Shenzhen Shifang Zhizao Technology Co ltd
Original Assignee
Shenzhen Shifang Zhizao Technology Co ltd
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Shenzhen Shifang Zhizao Technology Co ltd filed Critical Shenzhen Shifang Zhizao Technology Co ltd
Priority to CN202311722575.XA priority Critical patent/CN117409006B/en
Publication of CN117409006A publication Critical patent/CN117409006A/en
Application granted granted Critical
Publication of CN117409006B publication Critical patent/CN117409006B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Classifications

    • H—ELECTRICITY
    • H01—ELECTRIC ELEMENTS
    • H01M—PROCESSES OR MEANS, e.g. BATTERIES, FOR THE DIRECT CONVERSION OF CHEMICAL ENERGY INTO ELECTRICAL ENERGY
    • H01M10/00—Secondary cells; Manufacture thereof
    • H01M10/42—Methods or arrangements for servicing or maintenance of secondary cells or secondary half-cells
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/04—Architecture, e.g. interconnection topology
    • G06N3/0464—Convolutional networks [CNN, ConvNet]
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/08—Learning methods
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T5/00—Image enhancement or restoration
    • G06T5/50—Image enhancement or restoration using two or more images, e.g. averaging or subtraction
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/0002—Inspection of images, e.g. flaw detection
    • G06T7/0004—Industrial image inspection
    • G06T7/001—Industrial image inspection using an image reference approach
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/10—Segmentation; Edge detection
    • G06T7/11—Region-based segmentation
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/10—Segmentation; Edge detection
    • G06T7/13—Edge detection
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/10—Segmentation; Edge detection
    • G06T7/136—Segmentation; Edge detection involving thresholding
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/10—Segmentation; Edge detection
    • G06T7/149—Segmentation; Edge detection involving deformable models, e.g. active contour models
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/10—Segmentation; Edge detection
    • G06T7/187—Segmentation; Edge detection involving region growing; involving region merging; involving connected component labelling
    • H—ELECTRICITY
    • H01—ELECTRIC ELEMENTS
    • H01M—PROCESSES OR MEANS, e.g. BATTERIES, FOR THE DIRECT CONVERSION OF CHEMICAL ENERGY INTO ELECTRICAL ENERGY
    • H01M10/00—Secondary cells; Manufacture thereof
    • H01M10/42—Methods or arrangements for servicing or maintenance of secondary cells or secondary half-cells
    • H01M10/4285—Testing apparatus
    • H—ELECTRICITY
    • H01—ELECTRIC ELEMENTS
    • H01M—PROCESSES OR MEANS, e.g. BATTERIES, FOR THE DIRECT CONVERSION OF CHEMICAL ENERGY INTO ELECTRICAL ENERGY
    • H01M6/00—Primary cells; Manufacture thereof
    • H01M6/50—Methods or arrangements for servicing or maintenance, e.g. for maintaining operating temperature
    • H—ELECTRICITY
    • H01—ELECTRIC ELEMENTS
    • H01M—PROCESSES OR MEANS, e.g. BATTERIES, FOR THE DIRECT CONVERSION OF CHEMICAL ENERGY INTO ELECTRICAL ENERGY
    • H01M6/00—Primary cells; Manufacture thereof
    • H01M6/50—Methods or arrangements for servicing or maintenance, e.g. for maintaining operating temperature
    • H01M6/5083—Testing apparatus
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/10—Image acquisition modality
    • G06T2207/10048—Infrared image
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/20—Special algorithmic details
    • G06T2207/20081—Training; Learning
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/20—Special algorithmic details
    • G06T2207/20084—Artificial neural networks [ANN]
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/20—Special algorithmic details
    • G06T2207/20212—Image combination
    • G06T2207/20221—Image fusion; Image merging
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/30—Subject of image; Context of image processing
    • G06T2207/30242—Counting objects in image

Landscapes

  • Engineering & Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • Theoretical Computer Science (AREA)
  • General Physics & Mathematics (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • Manufacturing & Machinery (AREA)
  • Chemical & Material Sciences (AREA)
  • Chemical Kinetics & Catalysis (AREA)
  • Electrochemistry (AREA)
  • General Chemical & Material Sciences (AREA)
  • Software Systems (AREA)
  • Data Mining & Analysis (AREA)
  • Molecular Biology (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Artificial Intelligence (AREA)
  • Biomedical Technology (AREA)
  • Biophysics (AREA)
  • Computational Linguistics (AREA)
  • Mathematical Physics (AREA)
  • Evolutionary Computation (AREA)
  • General Health & Medical Sciences (AREA)
  • Health & Medical Sciences (AREA)
  • Computing Systems (AREA)
  • General Engineering & Computer Science (AREA)
  • Quality & Reliability (AREA)
  • Investigating Or Analyzing Materials Using Thermal Means (AREA)
  • Radiation Pyrometers (AREA)

Abstract

The application is applicable to the technical field of battery detection, and provides a bubble contour detection method, a film layer repair method, electronic equipment and a storage medium, wherein the bubble contour detection method comprises the following steps: acquiring an initial thermal image of a battery heating film and acquiring a target thermal image set after the battery heating film is heated, wherein the initial thermal image comprises a thermal image before the battery heating film is heated, and the target thermal image set comprises target thermal image images of the battery heating film after the battery heating film is heated in each preset cooling time period; determining a temperature abnormal region according to the initial thermal image and at least one target thermal image, and determining whether the temperature abnormal region is a bubble region according to a target thermal image set; if the temperature abnormal region is a bubble region, determining contour information of the bubble region according to the target thermal image set. In this application, confirm bubble profile through the thermography, help improving bubble profile detection's accuracy, help realizing the accurate restoration of rete.

Description

Bubble contour detection method, film repair method, electronic device and storage medium
Technical Field
The application belongs to the technical field of battery detection, and particularly relates to a bubble contour detection method, a membrane layer repair method, electronic equipment and a storage medium.
Background
In order to improve the performance of a battery in a low-temperature environment, a heating film is generally coated on the surface of the battery, and the temperature of the battery is increased by electrifying and heating the heating film. After the heating film is attached, air bubble detection is usually required to be carried out on the surface of the heating film, so that air bubbles exist between the battery and the heating film, the heat transfer effect of the heating film is prevented from being affected, and the outline of the air bubbles is an important index in the air bubble detection.
In the related art, when performing bubble detection, a certain pressure is generally applied to a heating film by an air pressure detecting device, a change in pressure value of the film surface is detected by a pressure sensor in the detecting device, and the bubble profile is determined based on the change in pressure value. When the mode is used for determining the outline of the bubble, certain pressure needs to be applied to the heating film, the bubble is likely to deform, the detected outline of the bubble is low in accuracy, and then when the film is repaired, the repair range is incomplete, or the repair range is too large, so that the accuracy of the film repair is affected.
Disclosure of Invention
The embodiment of the application provides a bubble contour detection method, a membrane layer repair method, electronic equipment and a storage medium, which can solve the technical problems that in the related technology, the detected bubble contour is low in accuracy, and further when a membrane layer is repaired, the repair range is incomplete or the repair range is too large, and the accuracy of the membrane layer repair is affected.
A first aspect of an embodiment of the present application provides a method for detecting a bubble profile, including:
acquiring an initial thermal image of a battery heating film and acquiring a target thermal image set after the battery heating film is heated, wherein the initial thermal image comprises a thermal image before the battery heating film is heated, and the target thermal image set comprises target thermal image images of the battery heating film after the battery heating film is heated in each preset cooling time period;
determining a temperature abnormal region according to the initial thermal image and at least one target thermal image, and determining whether the temperature abnormal region is a bubble region according to a target thermal image set;
if the temperature abnormal region is a bubble region, determining contour information of the bubble region according to the target thermal image set.
A second aspect of the embodiments of the present application provides a method for repairing a film layer, including:
Determining bubble characteristic information according to the contour information of the bubble area, wherein the bubble characteristic information comprises the number of bubbles and the bubble characteristic size;
selecting a target restoration mode corresponding to the bubble characteristic information from a plurality of preset restoration modes, and restoring the bubble area according to the target restoration mode.
A third aspect of the embodiments of the present application provides a bubble profile detection apparatus, including:
the image acquisition unit is used for acquiring an initial thermal image of the battery heating film and acquiring a target thermal image set after the battery heating film is heated, wherein the initial thermal image comprises a thermal image before the battery heating film is heated, and the target thermal image set comprises target thermal image images of the battery heating film after the battery heating film is heated and in each preset cooling time period;
the area determining unit is used for determining a temperature abnormal area according to the initial thermal image and at least one target thermal image, and determining whether the temperature abnormal area is a bubble area according to the target thermal image set;
and the contour determining unit is used for determining contour information of the bubble area according to the target thermal image set if the temperature abnormal area is the bubble area.
A fourth aspect of the embodiments of the present application provides a film repair device, including:
The feature determining unit is used for determining bubble feature information according to the outline information of the bubble area, wherein the bubble feature information comprises the number of bubbles and bubble feature sizes;
the film layer repairing unit is used for selecting a target repairing mode corresponding to the bubble characteristic information from a plurality of preset repairing modes and repairing the bubble area according to the target repairing mode.
A fifth aspect of the embodiments of the present application provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, where the processor implements the steps of the bubble profile detection method provided in the first aspect or the steps of the film repair method provided in the second aspect when the processor executes the computer program.
A sixth aspect of the embodiments of the present application provides a computer-readable storage medium storing a computer program, which when executed by a processor, implements the steps of the bubble profile detection method provided in the first aspect, or the steps of the film repair method provided in the second aspect.
The implementation of the bubble contour detection method, the film layer repair method, the device, the electronic equipment and the storage medium provided by the embodiment of the application has the following beneficial effects: the initial thermal image of the battery heating film and the heated target thermal image set are used for determining the temperature abnormal region of the battery heating film, determining whether the region is a bubble region or not based on the temperature abnormal region, and determining the outline information of the bubble region based on the target thermal image set when the temperature abnormal region is the bubble region, so that the accuracy of bubble outline detection is improved, the repair according to the accurate bubble outline during the repair of the film layer is facilitated, and the accurate repair of the film layer is facilitated.
It will be appreciated that the advantages of the second to sixth aspects may be found in the relevant description of the first aspect, and are not described here again.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following description will briefly introduce the drawings that are needed in the embodiments or the related technical descriptions, and it is obvious that the drawings in the following description are only some embodiments of the present application, and other drawings may be obtained according to these drawings without inventive effort for a person of ordinary skill in the art.
FIG. 1 is a flowchart of an implementation of a bubble profile detection method according to an embodiment of the present disclosure;
FIG. 2 is a flow chart of an implementation of determining a temperature anomaly region provided by an embodiment of the present application;
FIG. 3 is a flow chart of an implementation of determining bubble areas provided by an embodiment of the present application;
FIG. 4 is a flow chart of an implementation of determining profile information provided by an embodiment of the present application;
FIG. 5 is a flowchart illustrating an implementation of a method for repairing a membrane layer according to an embodiment of the present disclosure;
FIG. 6 is a block diagram of a bubble profile detection apparatus according to one embodiment of the present application;
FIG. 7 is a block diagram of a membrane repair device according to an embodiment of the present disclosure;
Fig. 8 is a block diagram of an electronic device according to an embodiment of the present application.
Detailed Description
In the following description, for purposes of explanation and not limitation, specific details are set forth, such as particular system configurations, techniques, etc. in order to provide a thorough understanding of the embodiments of the present application. It will be apparent, however, to one skilled in the art that the present application may be practiced in other embodiments that depart from these specific details. In other instances, detailed descriptions of well-known systems, devices, circuits, and methods are omitted so as not to obscure the description of the present application with unnecessary detail.
It should be understood that the terms "comprises" and/or "comprising," when used in this specification and the appended claims, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof.
As used in this specification and the appended claims, the term "if" may be interpreted as "when..once" or "in response to a determination" or "in response to detection" depending on the context. Similarly, the phrase "if a determination" or "if a [ described condition or event ] is detected" may be interpreted in the context of meaning "upon determination" or "in response to determination" or "upon detection of a [ described condition or event ]" or "in response to detection of a [ described condition or event ]".
Reference in the specification to "one embodiment" or "some embodiments" or the like means that a particular feature, structure, or characteristic described in connection with the embodiment is included in one or more embodiments of the application. Thus, appearances of the phrases "in one embodiment," "in some embodiments," "in other embodiments," and the like in the specification are not necessarily all referring to the same embodiment, but mean "one or more but not all embodiments" unless expressly specified otherwise. The terms "comprising," "including," "having," and variations thereof mean "including but not limited to," unless expressly specified otherwise.
In order to explain the technical aspects of the present application, the following examples are presented.
Referring to fig. 1, fig. 1 is a flowchart of an implementation of a method for detecting a bubble profile according to an embodiment of the present application, where the flowchart may include the following steps 101 to 103.
Step 101, acquiring an initial thermal image of the battery heating film, and acquiring a target thermal image set of the battery heating film after heating.
The initial thermal image may include a thermal image before heating the battery heating film, and the target thermal image set may include a target thermal image of the battery heating film after heating the battery heating film when each preset cooling period is reached. The preset cooling duration is usually a preset cooling duration, for example, cooling for 1 second, cooling for 2 seconds, cooling for 3 seconds, cooling for 4 seconds, and cooling for 5 seconds.
Wherein, each target thermal image in the initial thermal image and the target thermal image set is a thermal image of the battery heating film collected by the infrared sensor. Thermography is typically used to represent different temperatures of an object with different colors, thereby converting infrared radiation data of the object into a visible image. In practice, red and pink in the thermogram indicate a higher object temperature, and blue and green indicate a lower object temperature.
Here, the target thermal image set may include a plurality of target thermal images, where each preset cooling duration may correspond to at least one target thermal image. For example, the preset cooling duration includes cooling for 1 second, cooling for 2 seconds, cooling for 3 seconds, cooling for 4 seconds, cooling for 5 seconds, and the target thermal image set may include a target thermal image at cooling for 1 second, a target thermal image at cooling for 2 seconds, a target thermal image at cooling for 3 seconds, a target thermal image at cooling for 4 seconds, and a target thermal image at cooling for 5 seconds.
In the present embodiment, the execution subject of the above-described bubble profile detection method is typically an electronic device. The electronic device may be hardware or software. When the electronic device is hardware, the electronic device may be implemented as a distributed electronic device cluster formed by a plurality of electronic devices, or may be implemented as a single electronic device. When the electronic device is software, it may be implemented as a plurality of software or software modules, or may be implemented as a single software or software module, which is not specifically limited herein.
In practice, the executing body can be in communication connection with the infrared sensor, and before the battery heating film is electrified and heated, the executing body can send an image acquisition instruction to the infrared sensor to control the infrared sensor to acquire and upload a thermal image of the battery heating film, so that an initial thermal image of the battery heating film is acquired.
After the battery heating film is electrified and heated, the execution main body can respectively send image acquisition instructions to the infrared sensor after reaching each preset cooling time, and the infrared sensor is controlled to acquire and upload the thermal image of the battery heating film, so that a target thermal image of the battery heating film when each preset cooling time is obtained.
For example, after the battery heating film finishes heating, the battery heating film enters a cooling stage, and after cooling for 1 second, the executing body may send an image acquisition instruction to the infrared sensor to control the infrared sensor to acquire and upload a target thermal image when the battery heating film cools for 1 second; after cooling for 2 seconds, the execution main body can send an image acquisition instruction to the infrared sensor, and the infrared sensor is controlled to acquire and upload a target thermal image when the battery heating film is cooled for 2 seconds; after cooling for 3 seconds, the execution main body can send an image acquisition instruction to the infrared sensor, and the infrared sensor is controlled to acquire and upload a target thermal image when the battery heating film is cooled for 3 seconds; after cooling for 4 seconds, the execution main body can send an image acquisition instruction to the infrared sensor, and the infrared sensor is controlled to acquire and upload a target thermal image when the battery heating film is cooled for 4 seconds; after cooling for 5 seconds, the execution main body can send an image acquisition instruction to the infrared sensor, and the infrared sensor is controlled to acquire and upload a target thermal image of the battery heating film when cooling for 5 seconds, so that the execution main body can acquire the target thermal image of the battery heating film when each preset cooling time period.
And 102, determining a temperature abnormal region according to the initial thermal image and at least one target thermal image, and determining whether the temperature abnormal region is a bubble region according to the target thermal image set.
The abnormal temperature region is usually a region corresponding to a higher temperature than that of the adjacent region in the target thermal image.
The bubble region is generally a region where bubbles exist. In practice, when there is a bubble between the battery heating film and the battery cell, the heat generated by the battery heating film cannot be quickly conducted to the battery cell due to poor air thermal conductivity in the bubble, so that the temperature of the bubble area is increased, and a significant difference exists between the thermal image and the peripheral area.
In practice, the executing body may compare at least one target thermal image in the obtained target thermal image set with the initial thermal image, determine whether there is a region with higher temperature in the target thermal image, then compare the target thermal image corresponding to each preset cooling duration to obtain a duration higher duration of the temperature anomaly region, and then determine whether the temperature anomaly region is a bubble region based on the duration higher duration and the preset higher duration. The preset higher duration is usually a preset duration, for example, 2 seconds. If the duration of the continuous higher time length is greater than or equal to the preset higher time length, determining the temperature abnormal region as a bubble region; if the duration of the continuous higher time is smaller than the preset higher time, determining the temperature abnormal region as a non-bubble region.
For example, the preset duration of the higher time is 2 seconds, the execution subject compares the target thermal image at the time of cooling for 1 second with the target thermal image at the time of cooling for 2 seconds, the target thermal image at the time of cooling for 3 seconds, the target thermal image at the time of cooling for 4 seconds, and the target thermal image at the time of cooling for 5 seconds, determines that the duration of the temperature anomaly area in the target thermal image is 4 seconds, is longer than the preset duration, and determines that the temperature anomaly area is a bubble area.
And step 103, if the temperature abnormal region is a bubble region, determining contour information of the bubble region according to the target thermal image set.
The contour information is usually edge position information of the bubble area, and the contour information may include coordinate information of each pixel point corresponding to the bubble area.
In practice, if the temperature anomaly area is a bubble area, the execution body may use coordinate information of each pixel point corresponding to the contour of the bubble area as the contour information of the bubble area. Specifically, the executing body determines a point set corresponding to the bubble contour by using a preset contour recognition algorithm based on the obtained target thermal image, using two sides of the target thermal image as an X axis and a Y axis respectively and using an intersection point of the two sides as a coordinate origin, then calculating the distance between each point in the point set based on the coordinate origin, thereby obtaining coordinate information of each point of the bubble contour, and then using the coordinate information as contour information of the bubble region. The preset contour recognition algorithm may include at least one of the following: edge detection algorithms, thresholding algorithms, morphological algorithms, etc.
In practice, the execution subject may input the target thermal image into a pre-trained contour information determination model, and determine contour information of a bubble region in the target thermal image using the contour information determination model. The contour information determination model is used for representing the corresponding relation between the image and contour information of the bubble area presented in the image. Here, the profile information determination model may be a model obtained by training an initial model (for example, convolutional neural network (Convolutional Neural Network, CNN), residual network (ResNet), or the like) by a deep learning method based on training samples.
According to the bubble contour detection method, the initial thermal image of the battery heating film and the heated target thermal image set are used for determining the temperature abnormal region of the battery heating film, whether the region is the bubble region is determined based on the temperature abnormal region, and when the temperature abnormal region is the bubble region, contour information of the bubble region is determined based on the target thermal image set, so that accuracy of bubble contour detection is improved, repair according to accurate bubble contours during film repair is facilitated, and accurate repair of the film is facilitated.
Referring to fig. 2, fig. 2 is a flowchart of an implementation of determining a temperature anomaly area according to an embodiment of the present application, and the flowchart may include the following steps 201 to 203.
Step 201, determining an initial temperature value corresponding to each pixel point in the initial thermal image according to the initial thermal image and the initial color code information corresponding to the initial thermal image.
The initial color code information is used for indicating initial temperature values corresponding to various colors in the initial thermal image.
In practice, for each pixel point in the initial thermal image, the executing body can quickly determine the initial temperature value corresponding to the pixel point through the initial color code information.
Step 202, determining a target temperature value corresponding to each pixel point in the target thermal image according to the target thermal image and the target color code information corresponding to the target thermal image.
The target color code information is used for indicating initial temperature values corresponding to various colors in the target thermal image.
In practice, for each pixel point in the target thermal image, the executing body can quickly determine the target temperature value corresponding to the pixel point through the target color code information.
Step 203, for each pixel, determining a temperature difference of the pixel according to the initial temperature value and the target temperature value, and determining a temperature anomaly region according to the temperature difference and a preset temperature difference threshold.
Wherein the temperature difference is a difference between an initial temperature value and a target temperature value.
The preset temperature difference threshold is usually a preset temperature difference. In practice, when the temperature difference corresponding to a certain pixel point is greater than or equal to a preset temperature difference threshold value, the pixel point can be determined to belong to a point in the temperature abnormal region; when the temperature difference corresponding to a certain pixel point is smaller than the preset temperature difference threshold value, the pixel point can be determined to not belong to the point in the temperature abnormal region.
In practice, for each pixel, the executing body may determine whether the pixel belongs to a point in the temperature anomaly area by using the temperature difference of the pixel and a preset temperature difference threshold value, and determine an area to which each pixel belonging to the temperature anomaly area belongs as the temperature anomaly area.
In this embodiment, the initial temperature value and the target temperature value of each pixel point can be used to quickly determine the temperature difference corresponding to the corresponding pixel point, and the temperature difference and the preset temperature difference threshold can be used to quickly determine the point belonging to the temperature anomaly region, so that the temperature anomaly region in the whole thermal image is quickly determined, and the efficiency of determining the temperature anomaly region is improved.
Referring to fig. 3, fig. 3 is a flowchart of an implementation of determining a bubble area according to an embodiment of the present application, where the flowchart may include the following steps 301 to 302.
Step 301, determining a duration higher than a duration corresponding to the abnormal temperature region according to each target thermal image in the target thermal image set and a preset cooling duration corresponding to each target thermal image.
The preset duration time is used for indicating the duration time of the temperature anomaly area.
In practice, after determining the temperature anomaly region, the executing body may determine a duration of higher duration corresponding to the temperature anomaly region based on a preset cooling duration corresponding to each target thermal image. As an example, the temperature anomaly region a exists in the target thermal image corresponding to the temperature reduction time of 0 seconds, the temperature anomaly region a still exists in the target thermal image corresponding to the temperature reduction time of 1 second, the temperature anomaly region a disappears in the target thermal image corresponding to the temperature reduction time of 2 seconds, and the duration of 2 seconds corresponding to the temperature anomaly region a can be determined.
Step 302, determining whether the temperature abnormal region is a bubble region according to the continuous high duration and the preset high duration.
The preset higher duration is usually a preset duration, for example, 2 seconds. In practice, when the duration of the sustained period of time corresponding to the temperature anomaly region is greater than or equal to the preset duration of time, the temperature anomaly region can be determined to be a bubble region. When the duration of the continuous higher time corresponding to the temperature abnormal region is smaller than the preset higher time, the temperature abnormal region can be determined not to belong to the bubble region.
In practice, the execution body may compare the duration of the sustained higher time period corresponding to the temperature anomaly region with the preset higher time period, so as to quickly determine whether the temperature anomaly region is a bubble region.
In this embodiment, through each target thermal image in the target thermal image set and the preset cooling time period corresponding to each target thermal image, the continuous higher time period corresponding to the temperature abnormal region can be quickly determined, and based on the continuous higher time period and the preset higher time period, whether the temperature abnormal region is a bubble region can be quickly determined, so that the efficiency of determining whether the temperature abnormal region belongs to the bubble region can be improved.
Referring to fig. 4, fig. 4 is a flowchart of an implementation of determining profile information according to an embodiment of the present application, where the flowchart may include the following steps 401 to 405.
Step 401, determining a segmentation gray threshold according to the gray value of each pixel point in each target thermal image.
In practice, the executing body may determine an average gray value according to the gray value of each pixel point in the target thermal image, and use the average gray value as the dividing gray threshold of the target thermal image.
In practice, the execution subject may divide the target thermal image into a plurality of regions, and for each region, the average gray value of the pixel points in the region is used as the dividing gray threshold of the region.
And step 402, performing binary segmentation on each pixel point in the target thermal image according to the segmentation gray threshold value to obtain a binary image corresponding to the target thermal image.
Wherein, the gray value of each pixel point in the binary image is 0 or 255. In the binary image, the gray value of the bubble area is 255 and is black; the non-bubble area has a gray value of 0 and appears white.
In practice, the executing body may compare the gray value of each pixel point of the target thermal image with the divided gray threshold, set a pixel with a gray value lower than the divided gray threshold to 0, and set a pixel with a gray value higher than or equal to the divided gray threshold to 255, so as to obtain a binary image corresponding to the target thermal image.
As an example, the execution body may divide the target thermal image into a plurality of regions, and for each region, the execution body may compare the gray value of the pixel in the region with the divided gray threshold of the region, set a pixel having a gray value lower than the divided gray threshold to 0, set a pixel having a gray value higher than or equal to the divided gray threshold to 255, and obtain the binary image of the divided region. And executing the same operation on each region to obtain a binary image corresponding to the target thermal image.
And step 403, using the pixel points with the same corresponding gray values in the binary image as the same connected region, and marking the connected region of the binary image.
In practice, the same pixels in the binary image generally represent the same region, and the executing body may mark the connected region of the binary image by using the same pixels as the same connected region based on the gray value of each pixel in the binary image.
Step 404, determining the number of pixels of each communication area according to the communication area marks, and determining the target communication area corresponding to the bubble area according to the number of pixels of each communication area and a preset number threshold.
The preset number threshold is usually a preset number threshold. The preset number threshold may include a maximum number threshold and a minimum number threshold, where the maximum number threshold and the minimum number threshold are used to filter out too large or too small connected areas.
In practice, for each connected region, the execution body may compare the number of pixels in the connected region with a maximum number threshold and a minimum number threshold, and determine whether the number of pixels in the connected region falls within a range of the maximum number threshold and the minimum number threshold. If the communication area belongs to the bubble area, determining the communication area as a target communication area corresponding to the bubble area; if not, discarding the connected region.
And step 405, determining contour information according to the target communication area.
In practice, the execution body may draw a circumscribed rectangle of the target communication region, and determine boundary information of the circumscribed rectangle as outline information of the bubble region.
In practice, the execution body may draw a minimum circumcircle of the target communication region, and determine boundary information of the minimum circumcircle as contour information of the bubble region.
In this embodiment, first, the division gray threshold is determined according to the gray value of each pixel point in each target thermal image. And then, carrying out binary segmentation on each pixel point in the target thermal image according to the segmentation gray threshold value to obtain a binary image corresponding to the target thermal image. And then, taking the pixel points with the same corresponding gray values in the binary image as the same connected region, and marking the connected region of the binary image. And then, determining the number of pixels of each communication area according to the communication area marks, and determining the target communication area corresponding to the bubble area according to the number of pixels of each communication area and a preset number threshold value. Finally, determining the contour information according to the target communication area can realize accurate determination of the contour information of the bubble area.
Referring to fig. 5, fig. 5 is a flowchart illustrating an implementation of a film repair method according to an embodiment of the present application, where the flowchart may include the following steps 501 to 502.
In step 501, bubble feature information is determined according to contour information of a bubble area.
Wherein the bubble characteristic information includes the number of bubbles and the bubble characteristic size. Here, the number of bubbles is used to indicate the number of bubbles present on the battery heating film, and the bubble feature size is used to indicate the shape of the bubbles on the battery heating film, and when the bubble profile is circular, the bubble feature size includes a diameter size, and when the bubble profile is square, the bubble feature size includes a length size and a width size.
In practice, for each target thermal image corresponding to the preset cooling duration, the executing body may repair the bubble area by adopting the bubble profile detected by the bubble profile detection method.
In practice, the executing body may perform edge detection on the bubble area in the target thermal image, determine a pixel point set corresponding to a boundary line of the bubble area, and then use coordinate information of each pixel point in the pixel point set in the target thermal image as contour information of the bubble area. Then, the execution body may fuse the bubble areas on the target thermal image based on the profile information of each bubble area, and two or more bubble areas, in which the corresponding profile information is overlapped and the distance between adjacent bubble areas is smaller than a preset distance threshold, may be used as one bubble area. Then, the executing body may count the fused bubble areas to obtain the number of bubbles on the target thermographic image, and perform shape fitting based on the fused bubble profile to determine the shape and the corresponding feature size of the bubble profile.
Step 502, selecting a target restoration mode corresponding to the bubble feature information from a plurality of preset restoration modes, and restoring the bubble area according to the target restoration mode.
The preset repairing mode may include: hot pressing, hot needling, air jet, etc.
The hot pressing method is to put the battery on a preheated hot plate for hot pressing, so that bubbles are discharged from the blue film. Thermal needling is the process of puncturing air bubbles with a thermal needle and then expelling the air bubbles with pressure. The air-jet method is to jet high-pressure gas onto the surface of a heating film to discharge bubbles attached to the surface.
The target repairing mode is a repairing mode corresponding to the bubble characteristic information.
For example, in practice, a case where the number of bubbles is less than or equal to 2 is defined as a case where the number of bubbles is small; defining the situation that the number of bubbles is more than 2 and less than or equal to 5 as that the number of bubbles is more; the case where the number of bubbles is greater than 5 is defined as the failure of the battery heating film due to the excessive number of bubbles.
When the outline shape of the bubble is circular, the case where the diameter is less than or equal to 0.5mm is defined as the small size of the bubble; the case where the diameter is greater than 0.5mm and less than or equal to 2mm is defined as the bubble size being large; the case where the diameter is greater than 2mm is defined as failure of the heating film due to the excessively large diameter of the battery.
When the outline shape of the bubble is square, the situation that the length dimension is smaller than or equal to 1cm and the width dimension is smaller than or equal to 1mm is defined as that the bubble size is smaller; the situation that the length dimension is larger than 1cm and smaller than or equal to 2cm, and the width dimension is larger than 1mm and smaller than 3mm is defined as that the bubble size is larger; the case where the length dimension was greater than 2cm and the width dimension was greater than 3mm was defined as failure of the battery heating film due to the excessive bubble size.
In practice, each repairing mode corresponds to different bubble characteristic information, and is applicable to scenes with different bubble numbers and bubble characteristic sizes. The hot pressing method is suitable for the conditions of large surface bubble size and large bubble quantity, and can rapidly discharge bubbles under the action of heating and pressure, and the whole cell surface can be repaired. The hot needling method is suitable for the situation that the number of bubbles on the surface is 1 and the size of the bubbles is large, and the hot needling method discharges the bubbles by locally heating and puncturing the bubbles and then utilizing pressure. The air-jet method is suitable for the conditions of small surface bubble size and small bubble quantity, and can effectively discharge micro bubbles by jetting the whole surface with high-pressure gas.
In practice, the executing body may use profile information to find a target repair mode corresponding to the bubble feature information from a pre-established profile information-bubble feature information-film repair mode correspondence table. The profile information-bubble feature information-film repair mode correspondence table may be a correspondence table pre-established by the execution body and storing correspondence between a plurality of profile information, bubble feature information and film repair modes.
In practice, the failure of the battery heating film indicates that the bubble defect on the surface of the battery heating film is serious, and the execution main body can take off the battery heating film and re-paste the film.
According to the film repairing method provided by the embodiment, the accurate bubble outline is determined based on the bubble outline detecting method, the bubble characteristic information is determined through the accurate outline information, the target repairing mode corresponding to the bubble characteristic information is selected from the repairing modes, and the bubble area is repaired by adopting the target repairing mode, so that incomplete film repairing or overlarge repairing range can be avoided, and the accuracy of film repairing is improved.
In some embodiments, the above-mentioned film repair method may further include: and acquiring the bubble characteristic information of the repaired battery heating film, determining the repaired detection result information corresponding to the battery heating film according to the bubble characteristic information and preset bubble detection conditions, and outputting the detection result information.
The preset bubble detection conditions are generally preset conditions for evaluating whether the bubble detection is qualified, and the preset bubble detection conditions may include at least one of the following: the area of each bubble is smaller than 4 square millimeters, the maximum size of each bubble is smaller than or equal to 2 millimeters, and no more than 1 bubble is arranged in each 100mmx100mm area.
In practice, when the bubble characteristic information meets all preset bubble detection conditions, the bubble detection of the corresponding battery heating film is qualified; when the bubble characteristic information does not meet any preset bubble detection condition, the bubble detection of the heating film of the corresponding battery is not qualified.
In practice, after repairing the battery heating film by using the target repairing method, the executing body may use the bubble contour detection method to obtain contour information of the repaired battery heating film, and obtain bubble feature information of the battery heating film based on the contour information.
The execution body may then compare the bubble feature information with parameters in a predetermined bubble detection condition, generate detection result information based on the comparison result, and transmit the detection result information to a display device connected to the execution body.
In this embodiment, the detected result of the battery heating film is obtained by comparing the bubble characteristic information of the repaired battery heating film with various parameters in preset bubble detection conditions, and the detected result is output, so that a user can know the bubble detection result of the battery heating film conveniently, and the method is helpful for judging the corresponding film repairing mode based on the bubble detection result, and is helpful for the user to adjust the film repairing mode in time when the detected result is not ideal.
Referring to fig. 6, fig. 6 is a block diagram illustrating a bubble profile detection apparatus 600 according to an embodiment of the present application, including:
an image obtaining unit 601, configured to obtain an initial thermal image of the battery heating film, and obtain a target thermal image set of the battery heating film after heating, where the initial thermal image includes a thermal image before heating the battery heating film, and the target thermal image set includes a target thermal image of the battery heating film after heating the battery heating film for each preset cooling period;
a region determining unit 602, configured to determine a temperature anomaly region according to the initial thermal image and the at least one target thermal image, and determine whether the temperature anomaly region is a bubble region according to the target thermal image set;
And a contour determination unit 603, configured to determine contour information of the bubble region according to the target thermal image set if the temperature anomaly region is the bubble region.
In some embodiments, the region determination unit 602 may include an initial temperature determination module, a target temperature determination module, and an abnormal region determination module (not shown in the figures).
The initial temperature determining module is used for determining initial temperature values corresponding to all pixel points in the initial thermal image according to the initial thermal image and the initial color code information corresponding to the initial thermal image;
the target temperature determining module is used for determining a target temperature value corresponding to each pixel point in the target thermal image according to the target thermal image and the target color code information corresponding to the target thermal image;
the abnormal region determining module is used for determining the temperature difference of the pixel points according to the initial temperature value and the target temperature value and determining the temperature abnormal region according to the temperature difference and a preset temperature difference threshold value.
In some embodiments, the area determining unit 602 may further include a higher duration determining module and a bubble area determining module (not shown in the figure).
The high duration determining module is used for determining the continuous high duration corresponding to the temperature abnormal region according to each target thermal image in the target thermal image set and the preset cooling duration corresponding to each target thermal image;
The bubble area determining module is used for determining whether the temperature abnormal area is a bubble area according to the continuous higher time length and the preset higher time length.
In some embodiments, the contour determination unit 603 may include a threshold determination module, a binary map generation module, a region marking module, a region connectivity module, and an information determination module (not shown in the figures).
The threshold determining module is used for determining a segmentation gray level threshold according to the gray level value of each pixel point in each target thermal image;
the binary image generating module is used for carrying out binary segmentation on each pixel point in the target thermal image according to the segmentation gray threshold value to obtain a binary image corresponding to the target thermal image;
the region marking module is used for marking the connected region of the binary image by taking the pixel points with the same corresponding gray values in the binary image as the same connected region;
the area communication module is used for determining the number of pixels of each communication area according to the communication area marks and determining a target communication area corresponding to the bubble area according to the number of pixels of each communication area and a preset number threshold;
and the information determining module is used for determining the contour information according to the target communication area.
According to the bubble contour detection device, the initial thermal image of the battery heating film and the heated target thermal image set are used for determining the temperature abnormal region of the battery heating film, whether the region is a bubble region is determined based on the temperature abnormal region, and when the temperature abnormal region is the bubble region, contour information of the bubble region is determined based on the target thermal image set, so that accuracy of bubble contour detection is improved, repair according to accurate bubble contours during film repair is facilitated, and accurate repair of the film is facilitated.
Referring to fig. 7, fig. 7 is a block diagram illustrating a film repairing apparatus 700 according to an embodiment of the present application, including:
a feature determining unit 701, configured to determine bubble feature information according to profile information of a bubble area, where the bubble feature information includes a number of bubbles and a bubble feature size;
the film repairing unit 702 is configured to select a target repairing mode corresponding to the bubble feature information from a plurality of preset repairing modes, and repair the bubble area according to the target repairing mode.
In some embodiments, the apparatus may further include a detection information output unit (not shown in the drawings) for: and acquiring the bubble characteristic information of the repaired battery heating film, determining the repaired detection result information corresponding to the battery heating film according to the bubble characteristic information and preset bubble detection conditions, and outputting the detection result information.
According to the film layer repairing device, the accurate bubble outline is determined based on the bubble outline detecting device, the bubble characteristic information is determined through the accurate outline information, the target repairing mode corresponding to the bubble characteristic information is selected from the repairing modes, and the bubble area is repaired by adopting the target repairing mode, so that incomplete film layer repairing or overlarge repairing range can be avoided, and the accuracy of film layer repairing is improved.
It should be noted that, because the content of information interaction and execution process between the above devices/units is based on the same concept as the embodiment of the bubble contour detection method or the film layer repairing method in the present application, specific functions and technical effects thereof may be found in the embodiment of the bubble contour detection method or the film layer repairing method, which are not described herein again.
Referring to fig. 8, fig. 8 is a block diagram of a server 800 according to an embodiment of the present application, where the server 800 includes: at least one processor 801 (only one processor is shown in fig. 8), a memory 802, and a computer program 803 stored in the memory 802 and executable on the at least one processor 801, such as a bubble profile detection program. The processor 801 when executing the computer program 803 implements the steps of the embodiments of the respective bubble profile detection methods described above, or of the embodiments of the film repair method. The processor 801 executes the functions of the respective modules/units in the respective apparatus embodiments described above, for example, the functions of the image acquisition unit 601 to the contour determination unit 603 shown in fig. 6, or the functions of the feature determination unit 701 to the film repair unit 702 shown in fig. 7, when executing the computer program 803.
By way of example, the computer program 803 may be partitioned into one or more units, one or more units being stored in the memory 802 and executed by the processor 801 to complete the present application. One or more of the elements may be a series of computer program instruction segments capable of performing a specified function, which instruction segments describe the execution of the computer program 803 in the server 800. For example, the computer program 803 may be divided into an image acquisition unit, a region determination unit, a contour determination unit, or a feature determination unit, a film repair unit, and specific functions of each unit are described in the above embodiments, which are not described herein.
Server 800 may be a computing device such as a server, desktop computer, tablet computer, cloud server, mobile terminal, and the like. The server 800 may include, but is not limited to, a processor 801, a memory 802. It will be appreciated by those skilled in the art that fig. 8 is merely an example of a server 800 and is not intended to limit the server 800, and may include more or fewer components than shown, or may combine certain components, or different components, e.g., a server may further include input-output devices, network access devices, buses, etc.
The processor 801 may be a central processing unit (Central Processing Unit, CPU), but may also be other general purpose processors, digital signal processors (Digital Signal Processor, DSP), application specific integrated circuits (Application Specific Integrated Circuit, ASIC), off-the-shelf programmable gate arrays (Field-Programmable Gate Array, FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, or the like. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
Memory 802 may be an internal storage unit of server 800, such as a hard disk or memory of server 800. The memory 802 may also be an external storage device of the server 800, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card) or the like, which are provided on the server 800. Alternatively, the memory 802 may also include both internal storage units and external storage devices of the server 800. Memory 802 is used to store computer programs and other programs and data needed by server 800. The memory 802 may also be used to temporarily store data that has been output or is to be output.
In addition, each functional unit in each embodiment of the present application may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit. The integrated units may be implemented in hardware or in software functional units.
The integrated modules, if implemented in the form of software functional units and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the present application implements all or part of the flow of the method of the above embodiments, and a computer program that may be implemented by a computer program to instruct related hardware may be stored in a computer readable storage medium, where the computer program when executed by a processor may implement the steps of each of the method embodiments described above. Wherein the computer program comprises computer program code, which may be in the form of source code, object code, executable files or in some intermediate form, etc. The computer readable medium may include: any entity or device capable of carrying computer program code, a recording medium, a U disk, a removable hard disk, a magnetic disk, an optical disk, a computer Memory, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), an electrical carrier signal, a telecommunications signal, a software distribution medium, and so forth. It should be noted that the content of the computer readable medium can be appropriately increased or decreased according to the requirements of the jurisdiction's jurisdiction and the patent practice, for example, in some jurisdictions, the computer readable medium does not include electrical carrier signals and telecommunication signals according to the jurisdiction and the patent practice.
In the foregoing embodiments, the descriptions of the embodiments are emphasized, and in part, not described or illustrated in any particular embodiment, reference is made to the related descriptions of other embodiments.
The above embodiments are only for illustrating the technical solution of the present application, and are not limiting thereof; although the present application has been described in detail with reference to the foregoing embodiments, it should be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit and scope of the technical solutions of the embodiments of the present application, and are intended to be included in the scope of the present application.

Claims (9)

1. A bubble profile detection method, the method comprising:
acquiring an initial thermal image of a battery heating film and acquiring a target thermal image set after the battery heating film is heated, wherein the initial thermal image comprises a thermal image before the battery heating film is heated, the target thermal image set comprises target thermal image images acquired by the battery heating film in each preset cooling time period after the battery heating film is heated, and the target thermal image images are sequentially ordered according to acquisition time sequence;
Determining a temperature abnormal region according to the initial thermal image and at least one target thermal image, determining whether the temperature abnormal region is a bubble region according to the target thermal image set, comparing at least one target thermal image in the target thermal image set with the initial thermal image, determining whether a region with higher temperature exists in the target thermal image, and comparing the target thermal image corresponding to each preset cooling time length, namely comparing a target thermal image with a plurality of target thermal images with each preset cooling time length in a first preset cooling time length, determining the continuous higher time length of the temperature abnormal region, and determining whether the temperature abnormal region is a bubble region according to the continuous higher time length and the preset higher time length;
and if the temperature abnormal region is a bubble region, determining contour information of the bubble region according to the target thermal image atlas.
2. The bubble contour detection method according to claim 1, wherein the determining a temperature anomaly region from the initial thermal image and at least one of the target thermal images includes:
Determining an initial temperature value corresponding to each pixel point in the initial thermal image according to the initial thermal image and initial color code information corresponding to the initial thermal image;
determining a target temperature value corresponding to each pixel point in the target thermal image according to the target thermal image and target color code information corresponding to the target thermal image;
for each pixel point, determining a temperature difference of the pixel point according to the initial temperature value and the target temperature value, and determining the temperature abnormal region according to the temperature difference and a preset temperature difference threshold value.
3. The bubble contour detection method according to claim 1, wherein the determining contour information of the bubble region from the target thermographic image set comprises:
determining a segmentation gray threshold according to gray values of pixel points in each target thermal image;
performing binary segmentation on each pixel point in the target thermal image according to the segmentation gray threshold value to obtain a binary image corresponding to the target thermal image;
taking the pixel points with the same corresponding gray values in the binary image as the same communication area, and marking the communication area of the binary image;
determining the number of pixels of each communication area according to the communication area marks, and determining a target communication area corresponding to the bubble area according to the number of pixels of each communication area and a preset number threshold;
And determining the contour information according to the target communication area.
4. A film repair method, characterized in that the film repair method is based on the bubble profile detection method according to any one of claims 1 to 3, the film repair method comprising:
determining bubble characteristic information according to the contour information of the bubble area, wherein the bubble characteristic information comprises the number of bubbles and the bubble characteristic size;
selecting a target restoration mode corresponding to the bubble characteristic information from a plurality of preset restoration modes, and restoring the bubble area according to the target restoration mode.
5. The film repair method according to claim 4, further comprising, after the repairing the bubble region in the target repair manner:
and acquiring the bubble characteristic information of the repaired battery heating film, determining the repaired detection result information corresponding to the battery heating film according to the bubble characteristic information and preset bubble detection conditions, and outputting the detection result information.
6. A bubble profile detection apparatus, comprising:
the image acquisition unit is used for acquiring an initial thermal image of the battery heating film and acquiring a target thermal image set after the battery heating film is heated, wherein the initial thermal image comprises a thermal image before the battery heating film is heated, the target thermal image set comprises target thermal image images acquired by the battery heating film at each preset cooling time length of entering a cooling stage after the battery heating film is heated, and the target thermal image images are sequentially ordered according to an acquisition time sequence;
The area determining unit is used for determining a temperature abnormal area according to the initial thermal image and at least one target thermal image, determining whether the temperature abnormal area is a bubble area according to the target thermal image set, comparing at least one target thermal image in the target thermal image set with the initial thermal image, determining whether an area with higher temperature exists in the target thermal image, and comparing the target thermal image corresponding to each preset cooling duration, specifically comparing a target thermal image with a plurality of target thermal images with a first preset cooling duration, determining the duration of higher temperature abnormal area, and determining whether the temperature abnormal area is a bubble area according to the duration of higher temperature and the preset duration of higher temperature;
and the contour determining unit is used for determining contour information of the bubble area according to the target thermal image atlas if the temperature abnormal area is the bubble area.
7. A film repair device for use in a film repair method according to claim 4 or 5, the device comprising:
The characteristic determining unit is used for determining bubble characteristic information according to the contour information of the bubble area, wherein the bubble characteristic information comprises the number of bubbles and the bubble characteristic size;
and the film layer repairing unit is used for selecting a target repairing mode corresponding to the bubble characteristic information from a plurality of preset repairing modes and repairing the bubble area according to the target repairing mode.
8. An electronic device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor, when executing the computer program, implements the bubble profile detection method of any one of claims 1 to 3 or the film repair method of any one of claims 4 to 5.
9. A computer-readable storage medium storing a computer program, wherein the computer program, when executed by a processor, implements the bubble profile detection method according to any one of claims 1 to 3, or the film repair method according to any one of claims 4 to 5.
CN202311722575.XA 2023-12-15 2023-12-15 Bubble contour detection method, film repair method, electronic device and storage medium Active CN117409006B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202311722575.XA CN117409006B (en) 2023-12-15 2023-12-15 Bubble contour detection method, film repair method, electronic device and storage medium

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202311722575.XA CN117409006B (en) 2023-12-15 2023-12-15 Bubble contour detection method, film repair method, electronic device and storage medium

Publications (2)

Publication Number Publication Date
CN117409006A CN117409006A (en) 2024-01-16
CN117409006B true CN117409006B (en) 2024-04-05

Family

ID=89487445

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202311722575.XA Active CN117409006B (en) 2023-12-15 2023-12-15 Bubble contour detection method, film repair method, electronic device and storage medium

Country Status (1)

Country Link
CN (1) CN117409006B (en)

Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN101788502A (en) * 2010-01-14 2010-07-28 广州科易光电技术有限公司 Metal plate defect online detection method and device
CN112129780A (en) * 2020-09-24 2020-12-25 哈尔滨工业大学 BGA chip welding spot quality infrared nondestructive detection method
CN113706537A (en) * 2021-10-28 2021-11-26 江苏启力锻压机床有限公司 Irregular workpiece detection method based on machine vision
CN116125299A (en) * 2022-12-12 2023-05-16 浙江大华科技有限公司 Cell pack abnormality detection method, device, equipment and storage medium
CN116165239A (en) * 2022-12-12 2023-05-26 浙江凌骁能源科技有限公司 Method, device, system and storage medium for detecting adhesion quality of heating film
CN117173632A (en) * 2023-09-08 2023-12-05 中建西部建设北方有限公司 Surface bubble evaluation method and system for aluminum template concrete

Patent Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN101788502A (en) * 2010-01-14 2010-07-28 广州科易光电技术有限公司 Metal plate defect online detection method and device
CN112129780A (en) * 2020-09-24 2020-12-25 哈尔滨工业大学 BGA chip welding spot quality infrared nondestructive detection method
CN113706537A (en) * 2021-10-28 2021-11-26 江苏启力锻压机床有限公司 Irregular workpiece detection method based on machine vision
CN116125299A (en) * 2022-12-12 2023-05-16 浙江大华科技有限公司 Cell pack abnormality detection method, device, equipment and storage medium
CN116165239A (en) * 2022-12-12 2023-05-26 浙江凌骁能源科技有限公司 Method, device, system and storage medium for detecting adhesion quality of heating film
CN117173632A (en) * 2023-09-08 2023-12-05 中建西部建设北方有限公司 Surface bubble evaluation method and system for aluminum template concrete

Also Published As

Publication number Publication date
CN117409006A (en) 2024-01-16

Similar Documents

Publication Publication Date Title
CN110660066B (en) Training method of network, image processing method, network, terminal equipment and medium
CN109598721B (en) Defect detection method and device for battery pole piece, detection equipment and storage medium
CN107527028A (en) Target cell recognition methods, device and terminal
US11037016B2 (en) Feature determination apparatus and method adapted to multiple object sizes
CN114758249A (en) Target object monitoring method, device, equipment and medium based on field night environment
CN107563299A (en) A kind of pedestrian detection method using ReCNN integrating context informations
CN116503414B (en) Screen defect detection method, device, computer equipment and storage medium
CN111428552A (en) Black eye recognition method and device, computer equipment and storage medium
CN117409006B (en) Bubble contour detection method, film repair method, electronic device and storage medium
CN111461036A (en) Real-time pedestrian detection method using background modeling enhanced data
CN115511752A (en) BP neural network-based point coordinate distortion removal method and storage medium
CN113724243A (en) Image processing method, image processing device, electronic equipment and storage medium
CN116597343A (en) Expressway weather identification method and device based on ensemble learning algorithm
CN109614868A (en) Automobile tire decorative pattern graph line identifying system
CN117810570A (en) Recycling discharge control method and related device for waste lithium batteries
CN111489347A (en) Business license picture quality detection method and device, computer equipment and storage medium
CN114267031A (en) License plate detection method, license plate detection device, equipment terminal and readable storage medium
JP7428870B2 (en) Tire wear degree estimation device, tire wear degree learning device, tire wear degree estimation method, learned model generation method and program
CN118115530A (en) Vehicle track generation method and device, electronic equipment and storage medium
CN112541899B (en) Incomplete detection method and device of certificate, electronic equipment and computer storage medium
CN117405734A (en) Bubble position determining method and device, electronic equipment and storage medium
CN111649804A (en) Oil level monitoring method and device with storage function
CN117420426B (en) Contact state online evaluation method and system applied to GIS isolating switch
CN110633710A (en) Live pig disease early warning system
CN112560541A (en) Training device and method for target detection model and electronic equipment

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination
GR01 Patent grant
GR01 Patent grant