Lithium battery bottom surface defect detection method
Automatic Visual Pit Detection System for Bottom …
pit on the bottom metal surface is one of the important indicators of cylindrical lithium battery surface defect detection. ... for Bottom Surface of Cylindrical Lithium Battery January 2023 IEEE ...
A real-time method for detecting bottom defects of lithium batteries …
Defect detection of lithium batteries is a crucial step in lithium battery production. However, traditional detection methods mainly rely on the human eyes to observe the bottom defects of lithium battery products, which have low detection accuracy and slow detection speed. To solve this practical problem, an improved YOLOv5s model is …
Few-shot learning approach for 3D defect detection in lithium battery …
The accuracies of the experimental results are 93.3% for 10-shot batteries and 91.0% for 5-shot batteries, which means that our method can be used to classify the surface defects of lithium ...
Deep-Learning-Based Lithium Battery Defect Detection via Cross …
This research addresses the critical challenge of classifying surface defects in lithium electronic components, crucial for ensuring the reliability and safety of lithium batteries. With a scarcity of specific defect data, we introduce an innovative Cross-Domain Generalization (CDG) approach, incorporating Cross-domain Augmentation, Multi-task …
An end-to-end Lithium Battery Defect Detection Method Based on Detection …
AIA DETR model is proposed by adding AIA (attention in attention) module into transformer encoder part, which makes the model pay more attention to correct defect information so as to improve the detection ability of lithium battery surface defects. The DETR model is often affected by noise information such as complex backgrounds in the …
A YOLOv8-Based Approach for Real-Time Lithium-Ion Battery Electrode Defect Detection …
Abstract: Targeting the issue that the traditional target detection method has a high missing rate ... detecting surface defects of lithium batteries [12]. Presently, lithium battery electrode chip
A Surface Defect Detection Method Based on Positive Samples
2.1 Defect Repair Model Based on Positive SamplesThe inspiration for the model we have proposed comes from a series of GAN [] based repair and detection models.As shown in the Fig. 1 is the schematic diagram of …
Multi-task Deep Learning Based Defect Detection For Lithium Battery …
After the welding process of Lithium battery tabs, it is necessary to detect the surface defects of the welded products. The Gap is one of the common defects, and the defect forms are changeable, which brings a great challenge to the detection. This paper proposes a lithium battery tab gap defect technology based on multi-task deep learning model. …
3D Point Cloud-Based Lithium Battery Surface Defects Detection …
A 3D visual measurement system is a promising solution for detecting surface defects based on their roughness and height. This paper proposes an integrated …
Lithium battery surface defect detection based on the YOLOv3 detection …
With the continuous development of science and technology, cylindrical lithium batteries, as new energy batteries, are widely used in many fields. In the production process of lithium batteries, various defects may occur. To detect the defects of lithium batteries, a detection algorithm based on convolutional neural networks is proposed in this paper. …
An Automatic Defects Detection Scheme for Lithium-ion Battery Electrode Surface …
This paper presents an automatic flaw inspection scheme for online real-time detection of the defects on the surface of lithium-ion battery electrode (LIBE) in actual industrial production. Firstly, based on the conventional methods of region extraction, ROI (region of LIBE) could be extracted from the captured LIBE original image. Secondly, …
Research on detection algorithm of lithium battery surface defects based …
In this paper, the visual detection algorithm is studied to detect the defects such as pits, rust marks and broken skin on the surface of lithium battery, specifically to design the imaging experimental platform of lithium battery; use different lighting schemes to
Few-shot learning approach for 3D defect detection in lithium battery
The multi-exposure-based structured light method is introduced to reconstruct the 3D shape of the lithium battery using the MiniImageNet datasets as the source domain to pretrain the Cross-Domain Few-Shot Learning (CD-FSL) model. Detecting the surface defects in a lithium battery with an aluminium/steel shell is a difficult task. The effect of reflectivity, …
A novel approach for surface defect detection of lithium battery …
The pit on the bottom metal surface is one of the important indicators of cylindrical lithium battery surface defect detection. There are many complex factors in the detection of pit: …
Deep-Learning-Based Lithium Battery Defect Detection via Cross …
Deep-Learning-Based Lithium Battery Defect Detection via ...
Ultrasonic Tomography Study of Metal Defect Detection in Lithium-Ion Battery …
A separator was used to separate the defects on the cathode and anode surfaces. However, because this separator was relatively thin, the two sets of defects can be essentially considered to be located at the same depth in the battery. Table 1 summarizes the materials used to prepare the battery sample and their corresponding …
A real-time method for detecting bottom defects of lithium …
The experimental results show that the proposed method can effectively detect surface multiple types defects of lithium battery pole piece, and the average recognition rate of defects reaches 98.3%, which is an effective and feasible automatic …
Surface defect detection methods for industrial products with …
Surface defect detection methods for industrial products ...
Surface defect detection of cylindrical lithium-ion battery by …
Surface defect detection of cylindrical lithium-ion battery by ...
[PDF] Surface Defects Detection and Identification of Lithium Battery …
The experimental results show that the proposed method can effectively detect surface multiple types defects of lithium battery pole piece, and the average recognition rate of defects reaches 98.3%, which is an effective and feasible automatic defect detection and identification method. In order to realize the automatic detection of surface defects of …
Lithium battery surface defect detection based on the YOLOv3 …
The experimental results show that the mean average precision (mAP) value of the detection algorithm on the lithium battery validation dataset reaches 94% …
Image-based defect detection in lithium-ion battery electrode …
During the manufacturing of lithium-ion battery electrodes, it is difficult to prevent certain types of defects, which affect the overall battery performance and lifespan. Deep learning computer vision methods were used to evaluate the quality of lithium-ion battery electrode for automated detection of microstructural defects from light …
Defect detection method of lithium battery based on improved …
For the traditional algorithm to detect lithium battery defects, the missing rate is high and the speed is slow, an improved YOLOv7 algorithm was proposed. Firstly, CBAM attention mechanism is added to feature extraction part, which can enhance network''s representation ability. Secondly, in the feature fusion part, ConvNeXt …
A real-time method for detecting bottom defects of lithium …
Defect detection of lithium batteries is a crucial step in lithium battery production. However, traditional detection methods mainly rely on the human eyes to observe the …
State of the Art in Defect Detection Based on Machine Vision
State of the Art in Defect Detection Based on Machine Vision
Non-contact NDT methodology for defective battery. | Download …
In recent years, many new methods for defect detection of lithium batteries have emerged. Yi et al [1] built a noncontact ultrasonic scanning system with multi-channel to scan the battery sample ...
Defect detection method of lithium battery based on improved …
The results show that the optimization algorithm can improve the accuracy and speed of the lithium battery and achieves a 92.7% detection accuracy, surpassing the original network by 2.1%. For the traditional algorithm to detect lithium battery defects, the missing rate is high and the speed is slow, an improved YOLOv7 algorithm was …
An Automatic Defects Detection Scheme for Lithium-ion Battery Electrode Surface …
However, traditional detection methods mainly rely on the human eyes to observe the bottom defects of lithium battery products, which have low detection accuracy and slow detection speed.
Deep-Learning-Based Lithium Battery Defect Detection via Cross …
This research addresses the critical challenge of classifying surface defects in lithium electronic components, crucial for ensuring the reliability and safety of …
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