Surface Defects Detection and Identification of Lithium Battery Pole Piece Based on Multi-Feature Fusion and PSO-SVM

Xu, Changlu; Li, Linsheng; Li, Jiwei; Wen, Chuanbo

doi:10.1109/access.2021.3067641

Cited by 44 publications

(21 citation statements)

References 27 publications

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“…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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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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show abstract

“…At present, the adhesive structure defect detection method based on X-ray imaging, which is completed by manual participation in defect types identification, is not only difficult to ensure the accuracy of judgment due to certain subjectivity but also time-consuming and labor-intensive In recent years, additional image defect type identification methods have been adopted, including Threshold segmentation [1], Support Vector Machine (SVM) [2]- [4], and Artificial Neural Network(ANN) [5]. However, the above method is difficult to be applied to the recognition of multi-type defect feature images with no obvious difference in grey level, and the location information of defects cannot be detected.…”

Section: Introductionmentioning

confidence: 99%

Defect Identification of Adhesive Structure Based on DCGAN and YOLOv5

et al. 2022

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To overcome the problem of small defect samples and the imbalanced distribution of defect categories during adhesive structure defect detection, a defect identification approach based on DCGAN and YOLOv5 is proposed. The above problems are solved by fine-tuning the structure and the loss function of the DCGAN, generating high-quality defect images, and expanding the dataset of adhesive structure defects. Generally, using the EIOU loss function in the YOLOv5 network allows the network to converge faster during training and improve the recognition effect. In this article, it is illustrated by comparing with the GIOU loss function, that utilizing the EIOU loss function increases the mAP value by 3.9% and the recall by 10.5%, but the precision decreases. To solve this problem, the feature extraction capability of the network is enhanced by incorporating the CBAM after the C3 module in the YOLOv5 network. Interestingly, the mAP, precision, and recall of the optimized YOLOv5 algorithm are improved to 78.6%, 77.2%, and 76%, respectively, where the precision compared to the original model improved by 10.6%. This study has demonstrated that the improved YOLOv5 model can effectively detect adhesive structure defects, which provides defect identification and control theoretical research and technical support.

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“…Common defect types of automobile pipe joints include scratches, pits, and burrs. The causes of its defects are as follows: (1) surface damage caused by changes in hardness and stress state in the surface structure due to grinding heat and force; (2) surface damage caused by abrasion of the machining tool; (3) mechanical damage caused by collision and scratching.…”

Section: Introductionmentioning

confidence: 99%

“…At present, off-line manual inspection is still used to detect the surface defects of engine pipe joints. This kind of longterm repeated measurement is easily affected by personnel fatigue and subjective judgment, resulting in low efficiency and accuracy [1]. The application of machine vision inspection technology to the production line of automobile engine pipe joints can detect and classify surface defects of parts and components and help improve the automation and intelligence level of equipment [2].…”

Section: Introductionmentioning

confidence: 99%

Image Classification for Automobile Pipe Joints Surface Defect Detection Using Wavelet Decomposition and Convolutional Neural Network

Yang

Zhang

et al. 2022

IEEE Access

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The surface defect detection of automobile pipe joints based on computer vision faces technical challenges. The tiny-sized and smooth surfaces with processing textures will undermine the defect detection accuracy. In order to solve this problem, a new method was proposed, which combines wavelet decomposition and reconstruction with the canny operator to detect defects, and then uses the multi-channel fusion convolutional neural network to identify the types of defects. Firstly, illumination compensation technology is used to obtain a more uniform gray distribution of the original image. Then, the wavelet decomposition and reconstruction are used to remove noises and processing textures. Furthermore, the defect regions are segmented using the canny operator and hole filling from the image. Finally, the multichannel fusion convolutional neural network of decision-level is used to identify the surface defect types. This method provides an idea for the surface defects detection of automobile pipe joints with serious interference, such as smooth surface, random noises, and processing textures. The experimental results reveal that the method can effectively eliminate the influence of uneven illumination, random noises, and processing textures and achieve high defect classification accuracy.INDEX TERMS Automobile pipe joint, surface defect detection, wavelet decomposition and reconstruction, multi-channel fusion convolutional neural network.

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Surface Defects Detection and Identification of Lithium Battery Pole Piece Based on Multi-Feature Fusion and PSO-SVM

Cited by 44 publications

References 27 publications

A novel approach for surface defectdetection 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

Defect Identification of Adhesive Structure Based on DCGAN and YOLOv5

Image Classification for Automobile Pipe Joints Surface Defect Detection Using Wavelet Decomposition and Convolutional Neural Network

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