Lithium battery surface defect detection based on the YOLOv3 detection algorithm

Lang, Xianli; Shu, Shuangbao; Liang, Huajun; Zhang, Yuzhong

doi:10.1117/12.2615289

Cited by 6 publications

(4 citation statements)

References 1 publication

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“…Many researchers have worked on the problem of surface defection and proposed different solutions [8][9][10]. However, most of them have worked on the qualitative surface defect detection and few researchers study the quantitative detection of surface defect detection.…”

Section: Introductionmentioning

confidence: 99%

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A novel approach for surface defect detection of lithium battery based on improved K-nearest neighbor and Euclidean clustering segmentation

Liu

Guo

et al. 2023

Int J Adv Manuf Technol

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Surface defects of lithium batteries seriously affect the product quality and may lead to safety risks. In order to accurately identify the surface defects of lithium battery, a novel defect detection approach is proposed based on improved K-nearest neighbor (KNN) and Euclidean clustering segmentation. Firstly, an improved voxel density strategy for KNN is proposed to speed up the effect for point filtering. Then, the improved clustering segmentation strategy is applied to distinguish point clouds with defect features. The outline fitting algorithm based on the least square method is applied to determine geometric features of each surface defect which are used to classify defect types. Furthermore, experimental results show that the proposed surface defect detection method reaches 99.2% accuracy and 35.3-ms average time consumption for data processing. Finally, an industrial application example of lithium battery production is demonstrated, which meets the requirements of industrial application. All these reports exhibited that the industrial visual inspection system with rapid measurement is an effective method and guarantees for accelerating industrial production and manufacturing in the future.

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Section: Introductionmentioning

confidence: 99%

“…Fig 8. The influence of three parameters on the filtering effect: a the influence of parameter k on the filtering effect; b the influence of parameter d 0 on the filtering effect; c the influence of parameter ρ on the filtering effect…”

mentioning

confidence: 99%

A novel approach for surface defect detection of lithium battery based on improved K-nearest neighbor and Euclidean clustering segmentation

Liu

Guo

et al. 2023

Int J Adv Manuf Technol

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show abstract

“…Many researchers have worked on the problem of surface defection and proposed different solutions [4][5][6] . However, most of them have worked on the qualitative surface defect detection and few researchers study the quantitative detection of surface defect detection.…”

Section: Introductionmentioning

confidence: 99%

A novel approach for surface defectdetection of lithium battery based on improved K-Nearest Neighbor and Euclidean clustering segmentation

Liu¹,

Wu²,

Guo³

et al. 2023

Preprint

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Surface defects of lithium batteries seriously affect the product quality and may lead to safety risks. In order to accurately identify the surface defects of lithium battery, a novel defect detection approach is proposed based on improved K-Nearest Neighbor (KNN) and Euclidean clustering segmentation. Firstly, an improved Voxel density strategy for KNN is proposed to speed up the effect for point filtering. Then, the improved clustering segmentation strategy is applied to distinguish point clouds with defect features. The outline fitting algorithm based on least square method is applied to determine geometric features of each surface defects. Furthermore, experimental results show that the proposed surface defect detection method reaches 99.2% accuracy and 35.3ms average time consumption for data processing. Finally, an industrial application example of lithium battery production is demonstrated, which meets the requirements of industrial application.

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“…With the development of science and technology, lithium batteries with excellent performance have been widely used in many fields. However, there may be various surface defects in the production process of lithium batteries, which can produce many safety risks [1]. Therefore, the detection of surface defects of lithium batteries is particularly important in the early stage [2].…”

Section: Introductionmentioning

confidence: 99%

A Method for Extracting a Laser Center Line Based on an Improved Grayscale Center of Gravity Method: Application on the 3D Reconstruction of Battery Film Defects

Yao,

Wang,

et al. 2023

Applied Sciences

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Extraction of the laser fringe center line is a key step in the 3D reconstruction of linear structured light, the accuracy of which is directly related to the quality of the 3D model. A laser center line extraction method based on an improved gray center of gravity method is proposed to solve the problem of low extraction accuracy. Firstly, a smoothing method is used to eliminate the flat top of the laser line, and the Gaussian curve is adopted to fit the peak position of the curve. Then, the gray threshold is set to automatically extract the laser linewidth, and based on the window opening, the grayscale center of gravity method is improved to extract the coordinates of the center pixel for the second time. Finally, experiments show that the average absolute error of the improved laser line extraction method is 0.026 pixels, which is 2.3 times lower than the gray center of gravity method, 1.9 times lower than the curve fitting method, and the standard error can reach 0.005 pixels. Compared with the gray center of gravity method and the curve fitting method, the influence of gray value change on the center line extraction is more fully considered, and the center of the light strip can be extracted more accurately, achieving sub-pixel accuracy.

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Lithium battery surface defect detection based on the YOLOv3 detection algorithm

Cited by 6 publications

References 1 publication

A novel approach for surface defect detection of lithium battery based on improved K-nearest neighbor and Euclidean clustering segmentation

A novel approach for surface defect detection of lithium battery based on improved K-nearest neighbor and Euclidean clustering segmentation

A novel approach for surface defectdetection of lithium battery based on improved K-Nearest Neighbor and Euclidean clustering segmentation

A Method for Extracting a Laser Center Line Based on an Improved Grayscale Center of Gravity Method: Application on the 3D Reconstruction of Battery Film Defects

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