CN104655644A - Method and device for automatically detecting defects of lithium battery pole piece - Google Patents
Method and device for automatically detecting defects of lithium battery pole piece Download PDFInfo
- Publication number
- CN104655644A CN104655644A CN201510082341.2A CN201510082341A CN104655644A CN 104655644 A CN104655644 A CN 104655644A CN 201510082341 A CN201510082341 A CN 201510082341A CN 104655644 A CN104655644 A CN 104655644A
- Authority
- CN
- China
- Prior art keywords
- defect
- lithium battery
- battery pole
- image
- pole slice
- 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.)
- Pending
Links
- 230000007547 defect Effects 0.000 title claims abstract description 120
- WHXSMMKQMYFTQS-UHFFFAOYSA-N Lithium Chemical compound [Li] WHXSMMKQMYFTQS-UHFFFAOYSA-N 0.000 title claims abstract description 57
- 229910052744 lithium Inorganic materials 0.000 title claims abstract description 57
- 238000000034 method Methods 0.000 title claims description 15
- 238000001514 detection method Methods 0.000 claims abstract description 46
- 238000012545 processing Methods 0.000 claims description 28
- 230000000877 morphologic effect Effects 0.000 claims description 12
- 230000011218 segmentation Effects 0.000 claims description 12
- 238000001914 filtration Methods 0.000 claims description 6
- 238000012360 testing method Methods 0.000 claims description 6
- 238000002372 labelling Methods 0.000 claims 8
- 208000037656 Respiratory Sounds Diseases 0.000 claims 1
- 230000002950 deficient Effects 0.000 abstract description 6
- 238000012216 screening Methods 0.000 description 8
- 230000005540 biological transmission Effects 0.000 description 7
- 239000011888 foil Substances 0.000 description 5
- 239000002245 particle Substances 0.000 description 5
- 238000000576 coating method Methods 0.000 description 3
- 238000004519 manufacturing process Methods 0.000 description 3
- 238000003490 calendering Methods 0.000 description 2
- 239000011248 coating agent Substances 0.000 description 2
- 238000010586 diagram Methods 0.000 description 2
- 239000011149 active material Substances 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000007812 deficiency Effects 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 238000002360 preparation method Methods 0.000 description 1
- 239000002002 slurry Substances 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
Landscapes
- Investigating Materials By The Use Of Optical Means Adapted For Particular Applications (AREA)
- Battery Electrode And Active Subsutance (AREA)
Abstract
本发明公开了一种锂电池极片缺陷的自动检测方法，(1)离线收集锂电池极片的各种缺陷样本，获取对应的缺陷特征，得到缺陷特征库；(2)设置线阵CCD图像传感器的采集参数；(3)线阵CCD图像传感器开始采集待测锂电池极片的图像；(4)PC机对线阵CCD图像传感器采集到的图像进行缺陷检测：(5)重复步骤(3)至(4)直到对整个锂电池极片检测结束。本发明能自动检测锂电池极片缺陷区段并进行标记，具有智能化、效率高和准确度高的优点。
The invention discloses an automatic detection method for lithium battery pole piece defects. (1) collecting various defect samples of lithium battery pole pieces offline, acquiring corresponding defect features, and obtaining a defect feature library; (2) setting a linear array CCD image Acquisition parameters of the sensor; (3) the linear array CCD image sensor begins to collect the image of the lithium battery pole piece to be tested; (4) the PC carries out defect detection to the image collected by the linear array CCD image sensor: (5) repeat step (3 ) to (4) until the end of the detection of the entire lithium battery pole piece. The invention can automatically detect and mark the defective section of the pole piece of the lithium battery, and has the advantages of intelligence, high efficiency and high accuracy.
Description
技术领域technical field
本发明涉及光学摄像头图像缺陷检测技术法，特别涉及一种锂电池极片缺陷的自动检测方法及装置。The invention relates to an optical camera image defect detection method, in particular to an automatic detection method and device for a lithium battery pole piece defect.
背景技术Background technique
离子电池极片的制备通常是将将含有活性物质的浆料涂覆和/或填充在集电体上，干燥，压延，其中压延工艺包括将极片从压光机的两个辊轮之间的间隙通过，通过辊轮在极片上施加压力，调节压光机两个辊轮的辊轮间隙，准确地得到所需厚度的极片。但在涂覆压延工艺过程中极片常常出现涂料缺块，突起，露箔，划痕，裂纹，颗粒等缺陷。目前的检测方法主要依靠人眼检测，而不是在涂覆的工艺过程中形成一个自动检测环节。另外，极片需要进行双面涂覆，离线检测涉及对电池极片条带重新卷绕，人工和设备成本都将增加。The preparation of the ion battery pole piece is usually to coat and/or fill the slurry containing the active material on the current collector, dry, and calender, wherein the calendering process includes moving the pole piece from between the two rollers of the calender Pass through the gap, apply pressure on the pole piece through the roller, adjust the roller gap between the two rollers of the calender, and accurately obtain the required thickness of the pole piece. However, during the coating and calendering process, the pole pieces often have defects such as coating missing blocks, protrusions, exposed foil, scratches, cracks, and particles. The current detection method mainly relies on human eye detection, rather than forming an automatic detection link in the coating process. In addition, the pole piece needs to be coated on both sides, and the offline detection involves rewinding the battery pole piece strip, which will increase labor and equipment costs.
发明内容Contents of the invention
为了克服现有技术的上述缺点与不足，本发明的目的在于提供一种锂电池极片缺陷的自动检测方法，检测准确快捷、提高生产效率且节约成本。In order to overcome the above-mentioned shortcomings and deficiencies of the prior art, the purpose of the present invention is to provide an automatic detection method for lithium battery pole piece defects, which is accurate and fast, improves production efficiency and saves costs.
本发明的另一目的在于提供实现上述锂电池极片缺陷的自动检测方法的装置。Another object of the present invention is to provide a device for realizing the automatic detection method for the above-mentioned lithium battery pole piece defect.
本发明的目的通过以下技术方案实现：The object of the present invention is achieved through the following technical solutions:
一种锂电池极片缺陷的自动检测方法，包括以下步骤：An automatic detection method for a lithium battery pole piece defect, comprising the following steps:
(1)离线收集锂电池极片的各种缺陷样本，获取对应的缺陷特征，得到缺陷特征库；(1) Collect various defect samples of lithium battery pole pieces offline, obtain the corresponding defect features, and obtain the defect feature library;
(2)设置线阵CCD图像传感器的采集参数；(2) The acquisition parameters of the linear array CCD image sensor are set;
(3)线阵CCD图像传感器开始采集待测锂电池极片的图像；(3) The linear array CCD image sensor starts to collect the image of the lithium battery pole piece to be tested;
(4)PC机对线阵CCD图像传感器采集到的图像进行缺陷检测：(4) The PC performs defect detection on the image collected by the linear array CCD image sensor:
(4-1)对线阵CCD图像传感器采集到的图像进行阈值分割；(4-1) Carry out threshold segmentation to the image that linear array CCD image sensor gathers;
(4-2)缺块、突起缺陷检测：对(4-1)中阈值分割所得到的图像进行形态学处理，将进行形态学处理之后的图像与处理之前的图像做差分处理，在得到的差分图像中进行筛选，筛选出符合缺陷特征库中的缺陷特征的区段作为缺陷区段；(4-2) Detection of missing blocks and protruding defects: perform morphological processing on the image obtained by threshold segmentation in (4-1), and perform differential processing on the image after morphological processing and the image before processing, and obtain Screening is carried out in the differential image, and the segment conforming to the defect feature in the defect feature library is selected as the defect segment;
(4-3)露箔、划痕缺陷检测：对(4-1)中阈值分割所得到的图像进行中值滤波，对滤波之后的图像做动态阈值，接着对阈值得到的图像进行形态学处理，并与处理之前的图像做差分处理，在得到的差分图像中进行筛选，筛选出符合缺陷特征库中的缺陷特征的区段作为缺陷区段；(4-3) Foil and scratch defect detection: Perform median filtering on the image obtained by threshold segmentation in (4-1), perform dynamic thresholding on the filtered image, and then perform morphological processing on the image obtained by thresholding , and performing differential processing with the image before processing, and screening in the obtained differential image, and screening out the segment conforming to the defect feature in the defect feature library as the defect segment;
(4-4)裂纹、颗粒缺陷检测：将(4-1)中阈值分割所得到的图像拷贝并用锂电池极片上正确涂覆区段的灰度值赋值，将重新赋值之后的图像与赋值之前的图像进行差分处理，将得到的差分图像进行形态学处理，并在处理之后的区段筛选出符合缺陷特征库中的缺陷特征的区段作为缺陷区段；(4-4) Detection of cracks and particle defects: copy the image obtained by threshold segmentation in (4-1) and assign it with the gray value of the correctly coated section on the lithium battery pole piece, and compare the image after the reassignment with the image before the assignment Perform difference processing on the image, perform morphological processing on the obtained difference image, and filter out the segment that conforms to the defect feature in the defect feature library as the defect segment after the segment is processed;
(4-5)将(4-2)、(4-3)和(4-4)得到的缺陷区段进行合并处理，将合并处理之后所得的区段作为最终的缺陷区段；(4-5) Merge the defective segments obtained in (4-2), (4-3) and (4-4), and use the segment obtained after the merged processing as the final defective segment;
(5)重复步骤(3)至(4)直到对整个锂电池极片检测结束。(5) Steps (3) to (4) are repeated until the detection of the entire lithium battery pole piece is completed.
步骤(3)所述待测锂电池极片置于传送装置上，传送装置上方设有缺陷标记装置；The pole piece of the lithium battery to be tested in step (3) is placed on the conveying device, and a defect marking device is arranged above the conveying device;
在进行步骤(4-5)之后还进行以下步骤：Also carry out following steps after carrying out step (4-5):
(4-6)传送装置的传送速度恒定，线阵CCD图像传感器到缺陷标记装置的距离固定，计算出线阵CCD图像传感器当前采集的图像所对应的待测锂电池极片上的部位到达缺陷标记装置所需的时间；(4-6) The transmission speed of the transmission device is constant, the distance from the linear array CCD image sensor to the defect marking device is fixed, and the position on the lithium battery pole piece to be tested corresponding to the image currently collected by the linear array CCD image sensor is calculated to reach the defect marking device the time required;
(4-6)如果检测到缺陷，计算步骤(4-5)中所得到的缺陷区段相对于整帧图像的起点和终点位置、记录本次采集开始到当前的时长，根据以上结果，结合当前采集的图像所对应的待测锂电池极片上的部位到达缺陷标记装置所需的时间，计算出缺陷区段的起点和终点到达缺陷标记装置的时间；(4-6) If a defect is detected, calculate the start and end positions of the defect segment obtained in step (4-5) relative to the entire frame image, and record the time from the start of this acquisition to the current time. According to the above results, combined with The time required for the part on the pole piece of the lithium battery to be tested corresponding to the currently collected image to reach the defect marking device is calculated, and the time for the starting point and end point of the defect section to reach the defect marking device is calculated;
(4-7)根据(4-6)所得的结果，缺陷标记装置在延迟相应的时间之后，对待测锂电池极片的缺陷区段的起始位置分别进行标记。(4-7) According to the result obtained in (4-6), after a corresponding time delay, the defect marking device marks the initial positions of the defect segments of the lithium battery pole piece to be tested respectively.
实现上述方法的锂电池极片缺陷的自动检测装置，包括An automatic detection device for lithium battery pole piece defects realizing the above method, including
线阵CCD图像传感器，用于对待测锂电池极片进行图像采集；Linear array CCD image sensor, used for image acquisition of lithium battery pole pieces to be tested;
PC机，存储有缺陷特征库；用于对待测锂电池极片的图像进行处理，筛选出符合缺陷特征库中的缺陷特征的区段，得出检测结果。The PC is used to store the defect feature library; it is used to process the image of the lithium battery pole piece to be tested, to screen out the sections that meet the defect features in the defect feature library, and to obtain the test results.
所述线阵CCD图像为两个，分别用于对待测锂电池极片的上、下表面进行图像采集。There are two linear array CCD images, which are respectively used for image acquisition on the upper and lower surfaces of the lithium battery pole piece to be tested.
所述的锂电池极片缺陷的自动检测装置，还包括传送装置和缺陷标记装置；所述传送装置用于对待测锂电池极片进行传送；所述缺陷标记装置用于根据PC机的检测结果对待测锂电池极片缺陷区段的起始位置进行标记。The automatic detection device for the lithium battery pole piece defect also includes a transmission device and a defect marking device; the transmission device is used to transmit the lithium battery pole piece to be tested; Mark the starting position of the defect section of the lithium battery pole piece to be tested.
与现有技术相比，本发明具有以下优点和有益效果；Compared with the prior art, the present invention has the following advantages and beneficial effects;
1、本发明的方法对锂电池极片缺陷的识别准确度高，能够检测出极片常见的缺块，突起，露箔，划痕，裂纹，颗粒等缺陷。1. The method of the present invention has high accuracy in identifying defects in lithium battery pole pieces, and can detect common defects such as missing pieces, protrusions, exposed foil, scratches, cracks, and particles in pole pieces.
2、本发明的方法缺陷检测速度快，针对不同类型的缺陷，分别设计专门的检测算法进行检测，使得算法模型简单，检测速度加快，使得算法能在对于速度有一定要求的传输装置得到应用。2. The defect detection speed of the method of the present invention is fast. For different types of defects, special detection algorithms are designed for detection, so that the algorithm model is simple and the detection speed is accelerated, so that the algorithm can be applied to transmission devices that have certain requirements for speed.
3、本发明的方法由于检测过程无需人工参与，锂电池极片生产检测的自动化程度高，生产效率高，结构简单，成本低。3. Since the method of the present invention does not require manual participation in the detection process, the production and detection of lithium battery pole pieces has a high degree of automation, high production efficiency, simple structure, and low cost.
附图说明Description of drawings
图1为本发明的实施例的锂电池极片缺陷的自动检测装置的组成示意图。FIG. 1 is a schematic diagram of the composition of an automatic detection device for lithium battery pole piece defects according to an embodiment of the present invention.
图2为本发明的实施例的极片缺陷检测系统的组成示意图。FIG. 2 is a schematic diagram of the composition of a pole piece defect detection system according to an embodiment of the present invention.
图3为本发明的实施例的锂电池极片缺陷的自动检测方法的流程图。FIG. 3 is a flowchart of an automatic detection method for lithium battery pole piece defects according to an embodiment of the present invention.
图4为本发明的实施例的缺陷检测步骤的具体流程图。FIG. 4 is a specific flow chart of the defect detection step of the embodiment of the present invention.
具体实施方式Detailed ways
下面结合实施例，对本发明作进一步地详细说明，但本发明的实施方式不限于此。The present invention will be described in further detail below in conjunction with the examples, but the embodiments of the present invention are not limited thereto.
实施例Example
如图1所示，本实施例的锂电池极片缺陷的自动检测装置包括PC机1、线阵CCD图像传感器2、线性LED光源3、传送装置4、电池极片5和缺陷标记装置6。所述PC机1和两个分别位于电池极片正反两面的线阵CCD图像传感器2通过连接线连接，所述线性LED光源3与线阵CCD图像传感器2的相对位置固定。所述传送装置4用于传送电池极片5，所述缺陷标记装置6用于对任何一面有缺陷的极片区段进行标记。As shown in FIG. 1 , the automatic detection device for lithium battery pole piece defects in this embodiment includes a PC 1 , a linear array CCD image sensor 2 , a linear LED light source 3 , a conveying device 4 , a battery pole piece 5 and a defect marking device 6 . The PC 1 is connected to the two linear CCD image sensors 2 respectively located on the front and back sides of the battery pole piece through connecting wires, and the relative position between the linear LED light source 3 and the linear CCD image sensor 2 is fixed. The conveying device 4 is used for conveying the battery pole pieces 5, and the defect marking device 6 is used for marking the pole piece sections with defects on any one side.
PC机中设有极片缺陷检测系统、用于与线阵CCD图像传感器、缺陷标记装置进行通信的应用程序。The PC is equipped with a pole piece defect detection system, an application program for communicating with a linear array CCD image sensor and a defect marking device.
如图2所示，极片缺陷检测系统包括缺块、突起检测单元，露箔、划痕检测单元、裂纹、颗粒检测单元和合并处理单元。在极片缺陷检测的过程中，先用缺块、突起检测单元对极片缺陷进行检测，接着用露箔、划痕检测单元对极片的缺陷进行检测，接着用裂纹、颗粒检测单元对极片的缺陷进行检测，最后合并处理单元对以上三个单元所检测得到的缺陷区段进行合并处理。As shown in Figure 2, the pole piece defect detection system includes a missing block, protrusion detection unit, dew foil, scratch detection unit, crack, particle detection unit and a merge processing unit. In the process of pole piece defect detection, first use the missing block and protrusion detection unit to detect the pole piece defect, then use the exposed foil and scratch detection unit to detect the pole piece defect, and then use the crack and particle detection unit to detect the pole piece defect. The defects of the slices are detected, and finally the merging processing unit performs merging processing on the defective segments detected by the above three units.
如图3所示，本实施例的锂电池极片缺陷的自动检测方法，包括以下步骤：As shown in Figure 3, the automatic detection method of the lithium battery pole piece defect of the present embodiment comprises the following steps:
(1)离线收集锂电池极片的各种缺陷样本，获取对应的缺陷特征，得到缺陷特征库；(1) Collect various defect samples of lithium battery pole pieces offline, obtain the corresponding defect features, and obtain the defect feature library;
(2)设置线阵CCD图像传感器的采集参数；(2) The acquisition parameters of the linear array CCD image sensor are set;
(3)线阵CCD图像传感器开始采集待测锂电池极片的图像；(3) The linear array CCD image sensor starts to collect the image of the lithium battery pole piece to be tested;
(4)PC机对线阵CCD图像传感器采集到的图像进行缺陷检测，如图4所示：(4) The PC performs defect detection on the image collected by the linear array CCD image sensor, as shown in Figure 4:
(4-1)对线阵CCD图像传感器采集到的图像进行阈值分割；(4-1) Carry out threshold segmentation to the image that linear array CCD image sensor gathers;
(4-2)对(4-1)中阈值分割所得到的图像进行形态学处理，将进行形态学处理之后的图像与处理之前的图像做差分处理，在得到的差分图像中进行筛选，筛选出符合缺陷特征库中的缺陷特征的区段作为缺陷区段；(4-2) Perform morphological processing on the image obtained by the threshold segmentation in (4-1), perform differential processing on the image after the morphological processing and the image before processing, and perform screening in the obtained differential image, screening Select the segment conforming to the defect feature in the defect feature database as the defect segment;
(4-3)对(4-1)中阈值分割所得到的图像进行中值滤波，对滤波之后的图像做动态阈值，接着对阈值得到的图像进行形态学处理，并与处理之前的图像做差分处理，在得到的差分图像中进行筛选，筛选出符合缺陷特征库中的缺陷特征的区段作为缺陷区段；(4-3) Perform median filtering on the image obtained by threshold segmentation in (4-1), perform dynamic thresholding on the image after filtering, and then perform morphological processing on the image obtained by thresholding, and do the same with the image before processing Differential processing, screening in the obtained differential image, and screening out the segment conforming to the defect feature in the defect feature library as the defect segment;
(4-4)将(4-1)中阈值分割所得到的图像拷贝并用锂电池极片上正确涂覆区段的灰度值赋值，将重新赋值之后的图像与赋值之前的图像进行差分处理，将得到的差分图像进行形态学处理，并在处理之后的区段筛选出符合缺陷特征库中的缺陷特征的区段作为缺陷区段；(4-4) Copy the image obtained by threshold segmentation in (4-1) and assign it with the gray value of the correctly coated section on the lithium battery pole piece, and perform differential processing on the image after the reassignment and the image before the assignment, Performing morphological processing on the obtained differential image, and screening out the segment conforming to the defect feature in the defect feature library as the defect segment after processing;
(4-5)将(4-2)、(4-3)和(4-4)得到的缺陷区段进行合并处理，将合并处理之后所得的区段作为最终的缺陷区段；(4-5) Merge the defective segments obtained in (4-2), (4-3) and (4-4), and use the segment obtained after the merged processing as the final defective segment;
(4-6)传送装置的传送速度恒定，线阵CCD图像传感器到缺陷标记装置的距离固定，计算出线阵CCD图像传感器当前采集的图像所对应的待测锂电池极片上的部位到达缺陷标记装置所需的时间；(4-6) The transmission speed of the transmission device is constant, the distance from the linear array CCD image sensor to the defect marking device is fixed, and the position on the lithium battery pole piece to be tested corresponding to the image currently collected by the linear array CCD image sensor is calculated to reach the defect marking device the time required;
(4-6)如果检测到缺陷，计算步骤(4-5)中所得到的缺陷区段相对于整帧图像的起点和终点位置、记录本次采集开始到当前的时长，根据以上结果，结合当前采集的图像所对应的待测锂电池极片上的部位到达缺陷标记装置所需的时间，计算出缺陷区段的起点和终点到达缺陷标记装置的时间；(4-6) If a defect is detected, calculate the start and end positions of the defect segment obtained in step (4-5) relative to the entire frame image, and record the time from the start of this acquisition to the current time. According to the above results, combined with The time required for the part on the pole piece of the lithium battery to be tested corresponding to the currently collected image to reach the defect marking device is calculated, and the time for the starting point and end point of the defect section to reach the defect marking device is calculated;
(4-7)根据(4-6)所得的结果，缺陷标记装置在延迟相应的时间之后，对待测锂电池极片的缺陷区段的起始位置分别进行标记；(4-7) According to the result obtained in (4-6), after a corresponding time delay, the defect marking device marks the initial positions of the defect segments of the lithium battery pole piece to be tested respectively;
(5)重复步骤(3)至(4)直到对整个锂电池极片检测结束。(5) Steps (3) to (4) are repeated until the detection of the entire lithium battery pole piece is completed.
上述实施例为本发明较佳的实施方式，但本发明的实施方式并不受所述实施例的限制，其他的任何未背离本发明的精神实质与原理下所作的改变、修饰、替代、组合、简化，均应为等效的置换方式，都包含在本发明的保护范围之内。The above-mentioned embodiment is a preferred embodiment of the present invention, but the embodiment of the present invention is not limited by the embodiment, and any other changes, modifications, substitutions and combinations made without departing from the spirit and principle of the present invention , simplification, all should be equivalent replacement methods, and are all included in the protection scope of the present invention.
Claims (5)
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201510082341.2A CN104655644A (en) | 2015-02-13 | 2015-02-13 | Method and device for automatically detecting defects of lithium battery pole piece |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201510082341.2A CN104655644A (en) | 2015-02-13 | 2015-02-13 | Method and device for automatically detecting defects of lithium battery pole piece |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN104655644A true CN104655644A (en) | 2015-05-27 |
Family
ID=53247027
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201510082341.2A Pending CN104655644A (en) | 2015-02-13 | 2015-02-13 | Method and device for automatically detecting defects of lithium battery pole piece |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN104655644A (en) |
Cited By (19)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104916814A (en) * | 2015-06-26 | 2015-09-16 | 宁德时代新能源科技有限公司 | Processing system and method for lithium powder of lithium ion battery pole piece |
| CN108732507A (en) * | 2018-05-21 | 2018-11-02 | 中国科学技术大学 | A kind of lithium battery defect detecting device based on battery temperature field and visible images |
| CN108956626A (en) * | 2018-07-23 | 2018-12-07 | 广州超音速自动化科技股份有限公司 | Tab welding point defect detection device and its detection method |
| CN109324060A (en) * | 2018-10-11 | 2019-02-12 | 广东德尔智慧工厂科技有限公司 | A kind of lithium electropaining cloth surface defects detection system |
| CN109358070A (en) * | 2018-08-31 | 2019-02-19 | 广州超音速自动化科技股份有限公司 | Pole piece detection method, electronic equipment, storage medium and system |
| CN109580652A (en) * | 2018-10-30 | 2019-04-05 | 广州超音速自动化科技股份有限公司 | A kind of quality of battery pole piece detection method, electronic equipment and storage medium |
| CN109598721A (en) * | 2018-12-10 | 2019-04-09 | 广州市易鸿智能装备有限公司 | Defect inspection method, device, detection device and the storage medium of battery pole piece |
| CN109709102A (en) * | 2019-01-17 | 2019-05-03 | 广州超音速自动化科技股份有限公司 | Die-cutting machine lithium battery pole slice physical imperfection detection system, method and device |
| CN109741323A (en) * | 2019-01-09 | 2019-05-10 | 广州市顶丰自动化设备有限公司 | Pole piece detection method, device, computer equipment and the storage medium of lithium battery |
| CN109870459A (en) * | 2019-02-21 | 2019-06-11 | 武汉武大卓越科技有限责任公司 | The track plates crack detection method of non-fragment orbit |
| CN109886918A (en) * | 2019-01-14 | 2019-06-14 | 广州量子激光智能装备有限公司 | Wire drawing burr detection method, electronic equipment and the storage medium of lithium battery pole slice |
| CN110288584A (en) * | 2019-06-27 | 2019-09-27 | 常州固高智能装备技术研究院有限公司 | Ceramic hot-dip aluminizing detection method of surface flaw and device based on machine vision |
| CN110554053A (en) * | 2019-08-12 | 2019-12-10 | 广州超音速自动化科技股份有限公司 | Battery coating defect detection equipment |
| CN110943198A (en) * | 2019-12-04 | 2020-03-31 | 信义电源(苏州)有限公司 | Lithium battery sheet-making equipment |
| CN112213315A (en) * | 2019-06-25 | 2021-01-12 | 欧姆龙株式会社 | Appearance inspection management system, device, method and storage medium |
| CN112455098A (en) * | 2019-09-09 | 2021-03-09 | 无锡先导智能装备股份有限公司 | Pole piece marking method and pole piece marking system |
| CN112669295A (en) * | 2020-12-30 | 2021-04-16 | 上海电机学院 | Lithium battery pole piece defect detection method based on secondary threshold segmentation theory |
| CN113739751A (en) * | 2021-08-26 | 2021-12-03 | 东莞市创明电池技术有限公司 | Battery pole piece winding production quality detection method |
| CN115993104A (en) * | 2023-03-23 | 2023-04-21 | 杭州百子尖科技股份有限公司 | Method for positioning thickness measuring defect code spraying of new energy battery pole piece production line |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JPH09145624A (en) * | 1995-11-27 | 1997-06-06 | Ricoh Co Ltd | Cylindrical object defect detection device |
| JPH11183313A (en) * | 1997-12-16 | 1999-07-09 | Mega Float Gijutsu Kenkyu Kumiai | Flaw detecting device for large watertight structure |
| CN202362253U (en) * | 2011-11-28 | 2012-08-01 | 深圳市鹰眼在线电子科技有限公司 | On-line visual detection system for coating quality of battery pole piece |
| CN202921574U (en) * | 2012-10-23 | 2013-05-08 | 北京中天荣泰科技发展有限公司 | Automatic vision detection system for lithium battery pole piece |
| CN103499585A (en) * | 2013-10-22 | 2014-01-08 | 常州工学院 | Non-continuity lithium battery thin film defect detection method and device based on machine vision |
| CN104142350A (en) * | 2013-11-28 | 2014-11-12 | 中航锂电（洛阳）有限公司 | Device for detecting surface defects of lithium battery pole pieces |
-
2015
- 2015-02-13 CN CN201510082341.2A patent/CN104655644A/en active Pending
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JPH09145624A (en) * | 1995-11-27 | 1997-06-06 | Ricoh Co Ltd | Cylindrical object defect detection device |
| JPH11183313A (en) * | 1997-12-16 | 1999-07-09 | Mega Float Gijutsu Kenkyu Kumiai | Flaw detecting device for large watertight structure |
| CN202362253U (en) * | 2011-11-28 | 2012-08-01 | 深圳市鹰眼在线电子科技有限公司 | On-line visual detection system for coating quality of battery pole piece |
| CN202921574U (en) * | 2012-10-23 | 2013-05-08 | 北京中天荣泰科技发展有限公司 | Automatic vision detection system for lithium battery pole piece |
| CN103499585A (en) * | 2013-10-22 | 2014-01-08 | 常州工学院 | Non-continuity lithium battery thin film defect detection method and device based on machine vision |
| CN104142350A (en) * | 2013-11-28 | 2014-11-12 | 中航锂电（洛阳）有限公司 | Device for detecting surface defects of lithium battery pole pieces |
Non-Patent Citations (1)
| Title |
|---|
| 刘妍: "基于机器视觉的锂电池极片涂布缺陷检测系统设计", 《中国优秀硕士学位论文全文数据库信息科技辑》 * |
Cited By (22)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104916814A (en) * | 2015-06-26 | 2015-09-16 | 宁德时代新能源科技有限公司 | Processing system and method for lithium powder of lithium ion battery pole piece |
| CN108732507A (en) * | 2018-05-21 | 2018-11-02 | 中国科学技术大学 | A kind of lithium battery defect detecting device based on battery temperature field and visible images |
| CN108956626A (en) * | 2018-07-23 | 2018-12-07 | 广州超音速自动化科技股份有限公司 | Tab welding point defect detection device and its detection method |
| CN109358070A (en) * | 2018-08-31 | 2019-02-19 | 广州超音速自动化科技股份有限公司 | Pole piece detection method, electronic equipment, storage medium and system |
| CN109324060A (en) * | 2018-10-11 | 2019-02-12 | 广东德尔智慧工厂科技有限公司 | A kind of lithium electropaining cloth surface defects detection system |
| CN109580652A (en) * | 2018-10-30 | 2019-04-05 | 广州超音速自动化科技股份有限公司 | A kind of quality of battery pole piece detection method, electronic equipment and storage medium |
| CN109598721A (en) * | 2018-12-10 | 2019-04-09 | 广州市易鸿智能装备有限公司 | Defect inspection method, device, detection device and the storage medium of battery pole piece |
| CN109598721B (en) * | 2018-12-10 | 2021-08-31 | 广州市易鸿智能装备有限公司 | Defect detection method and device for battery pole piece, detection equipment and storage medium |
| CN109741323A (en) * | 2019-01-09 | 2019-05-10 | 广州市顶丰自动化设备有限公司 | Pole piece detection method, device, computer equipment and the storage medium of lithium battery |
| CN109886918A (en) * | 2019-01-14 | 2019-06-14 | 广州量子激光智能装备有限公司 | Wire drawing burr detection method, electronic equipment and the storage medium of lithium battery pole slice |
| CN109709102A (en) * | 2019-01-17 | 2019-05-03 | 广州超音速自动化科技股份有限公司 | Die-cutting machine lithium battery pole slice physical imperfection detection system, method and device |
| CN109870459A (en) * | 2019-02-21 | 2019-06-11 | 武汉武大卓越科技有限责任公司 | The track plates crack detection method of non-fragment orbit |
| CN109870459B (en) * | 2019-02-21 | 2021-07-06 | 武汉光谷卓越科技股份有限公司 | Track slab crack detection method for ballastless track |
| CN112213315A (en) * | 2019-06-25 | 2021-01-12 | 欧姆龙株式会社 | Appearance inspection management system, device, method and storage medium |
| CN110288584A (en) * | 2019-06-27 | 2019-09-27 | 常州固高智能装备技术研究院有限公司 | Ceramic hot-dip aluminizing detection method of surface flaw and device based on machine vision |
| CN110288584B (en) * | 2019-06-27 | 2023-06-23 | 常州固高智能装备技术研究院有限公司 | Ceramic hot dip aluminizing surface defect detection method and device based on machine vision |
| CN110554053A (en) * | 2019-08-12 | 2019-12-10 | 广州超音速自动化科技股份有限公司 | Battery coating defect detection equipment |
| CN112455098A (en) * | 2019-09-09 | 2021-03-09 | 无锡先导智能装备股份有限公司 | Pole piece marking method and pole piece marking system |
| CN110943198A (en) * | 2019-12-04 | 2020-03-31 | 信义电源(苏州)有限公司 | Lithium battery sheet-making equipment |
| CN112669295A (en) * | 2020-12-30 | 2021-04-16 | 上海电机学院 | Lithium battery pole piece defect detection method based on secondary threshold segmentation theory |
| CN113739751A (en) * | 2021-08-26 | 2021-12-03 | 东莞市创明电池技术有限公司 | Battery pole piece winding production quality detection method |
| CN115993104A (en) * | 2023-03-23 | 2023-04-21 | 杭州百子尖科技股份有限公司 | Method for positioning thickness measuring defect code spraying of new energy battery pole piece production line |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN104655644A (en) | Method and device for automatically detecting defects of lithium battery pole piece | |
| RU2665806C2 (en) | Method and device for investigating inspection system used for detecting surface defects | |
| CN103868935A (en) | Cigarette appearance quality detection method based on computer vision | |
| CN110084787B (en) | Synthetic leather gluing online monitoring method based on machine vision | |
| CN106248686A (en) | Glass surface defects based on machine vision detection device and method | |
| CN104237252A (en) | Machine-vision-based method and device for intelligently detecting surface micro-defects of product | |
| CN103630544B (en) | A kind of vision on-line detecting system | |
| CN107876425A (en) | A kind of bearing defect detecting system device of view-based access control model | |
| CN203981604U (en) | Conical bearing roller surface flaw inspection device | |
| CN109376773A (en) | Crack detection method based on deep learning | |
| CN103454285A (en) | Transmission chain quality detection system based on machine vision | |
| CN102221559A (en) | Online automatic detection method of fabric defects based on machine vision and device thereof | |
| CN103674968A (en) | Method and device for evaluating machine vision original-value detection of exterior corrosion appearance characteristics of material | |
| CN103837097B (en) | A kind of workpiece angle self-operated measuring unit based on image procossing and measuring method | |
| WO2023168984A1 (en) | Area-array camera-based quality inspection method and system for cathode copper | |
| CN102680494B (en) | Based on arcuation face, the polishing metal flaw real-time detection method of machine vision | |
| CN110554052A (en) | artificial board surface defect detection method and system | |
| CN107328781A (en) | A kind of columnar product detection method of surface flaw and device based on machine vision | |
| CN106596562A (en) | Device and method for online detection of magnetic ring appearance quality based on machine vision | |
| CN108693195A (en) | Pollution severity of insulators degree measurement method, apparatus and system | |
| CN103743756A (en) | Online detecting device and method for inner wall of vessel | |
| CN210775265U (en) | Artificial board surface defect detection system | |
| CN111024715B (en) | Method and device for extracting bottom plane of rubber road | |
| CN109622404B (en) | A system and method for automatic sorting of micro workpieces based on machine vision | |
| CN108180871A (en) | A kind of method of quantitative assessment surface of composite insulator dusting roughness |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| C06 | Publication | ||
| PB01 | Publication | ||
| C10 | Entry into substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| WD01 | Invention patent application deemed withdrawn after publication |
Application publication date: 20150527 |
|
| WD01 | Invention patent application deemed withdrawn after publication |