With a scarcity of specific defect data, we introduce an innovative Cross-Domain Generalization (CDG) approach, incorporating Cross-domain Augmentation, Multi-task Learning, and Iteration
View moreThis article takes the appearance defect detection of battery pack blue film as an example, focusing on introducing that Huahan Weiye uses 2.5D+AI to solve industrial testing difficulties. Appearance defect detection of lithium battery pack after blue film. 1.
View moreAutomatic Optical Inspection Machine for Hard Capsule Appearance Defects is applied to appearance defect detection, internal defect detection and color sorting of 00 # to 4 # empty capsules in capsule manufacturing plants and pharmaceutical plants. appearance detection (positive battery, negative battery, the side shell), battery scanning
View moreDuring 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 microscopy images of the sectioned
View moreIn 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 design different battery positioning and extraction algorithms; use Hough
View morelearning and defect detection using deep learning. X. R. Zeng[1] used the Sobel template algorithm to detect the surface of cylindrical bare-shell batteries. Z. C. Kang[2] suggested a pit detection approach that combines density detection,
View moreThe experimental results show that the proposed YOLO-MDD has a mean average precision of 80% for the defect detection of the lithium battery shells, especially with a
View moreAppearance Surface Defect Detection on Cylindrical Lithium-Ion Battery Using Deep Residual Networks with Transfer Learning - da62b207/LiIonDefDet-
View moreWith 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. Firstly, image
View moreDiscover industrial CT inspection for batteries. The Battery Analysis Module in Voyager provides advanced tools specifically designed for the inspection and quality control of battery cells, including cylindrical, pouch, and prismatic types. It features automated measurements for key characteristics like Anode-Cathode Overhang (ACO) distance, debris detection, and can wall
View moreIn the related art, the last appearance detection procedure before finished battery cell blanking is related to quality control of finished battery cell blanking, appearance defects of the battery cells are detected by using appearance detection equipment, but the existing appearance detection equipment can only carry out appearance detection on the standing battery cells, the detection
View moreGiven the increasing use of lithium-ion batteries, which is driven in particular by electromobility, the characterization of cells in production and application plays a
View more关键词: 锂离子电池, 缺陷检测, Co-DETR, 特征感知与融合网络, Shape IoU损失 Abstract: To address the challenges arising from the large scale and shape differences in defects on the end face of lithium battery casings, which make the identification of small target defects difficult, we propose a lithium battery surface defect detection algorithm based on BDD-DETR (Battery
View moreThe invention provides a method and a system for detecting appearance defects of a battery module based on deep learning, wherein the method comprises the following steps: obtaining...
View moreThe aluminum laminate pouch of pouch batteries is highly prone to deformation, which can cause various surface defects, thereby affecting their service life and potentially posing safety hazards. To address this problem, we propose an algorithm named YOLOv8-UCB for detecting surface defects in pouch batteries, which is based on the YOLOv8 model. First, while retaining the
View moreElectronics 2024, 13, 173 3 of 16 Initially introduced by Joseph et al. in 2016, the YOLO (You Only Look Once) algo-rithm marked a significant advancement in object detection.
View moreAfter 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. The model takes U-Net
View moreWith the promotion of the green transformation of China''s energy structure, lithium-ion batteries (LIBs) have been widely used in electric vehicles, consumer electronics and energy storage because of their high energy density and excellent cycle performance(Lu et al., 2013, Winter et al., 2018).Although the technology related to lithium batteries has made great
View moreAppearance Surface Defect Detection on Cylindrical Lithium-Ion Battery Using Deep Residual Networks with Transfer Learning - da62b207/LiIonDefDet- Appearance Surface Defect Detection on Cylindrical Lithium-Ion Battery Using Deep Residual Networks with Transfer Learning A python file named with "ResNet101_model_for
View moreLaser welding is a thermal conversion process; therefore, the parameters and workpieces must be extremely precise. Minor deviations in the welding process can result in serious defects, like collapse, cracks, porosity, burn, welding hole, etc, thus affecting the quality of the welding process [7], [8] addition, welding quality is also affected by the types of welding
View moreWhen and why does a rechargeable battery lose capacity or go bad? Mohammadi, M., Schauerman, C.M. et al. Rechargeable lithium-ion cell state of charge and defect detection by in-situ inside
View moreA 3D visual measurement system is a promising solution for detecting surface defects based on their roughness and height. This paper proposes an integrated approach to
View moreA YOLOv8-Based Approach for Real-Time Lithium-Ion Battery DOI: 10.3390/electronics13010173 Corpus ID: 266721264; A YOLOv8-Based Approach for Real-Time Lithium-Ion Battery Electrode Defect Detection with High Accuracy @article{Zhou2023AYA, title={A YOLOv8-Based Approach for Real-Time Lithium-Ion Battery Electrode Defect
View moreThis study is first time to scan and analyze different types of defects inside a battery by using ultrasonic technology, and it shows the detection capability boundary of
View moreHowever, there are many types of defects in the appearance of prismatic cells, including blue film bubbles, dents and bumps, wrinkles, breakage, and 40 types of other defects; a wide variety of
View moreIn the proposed Lithium-ion battery Surface Defect Detection (LSDD) system, an augmented dataset of multi-scale patch samples generated from a small number of lithium-ion battery images is used in the learning process of a two-stage classification scheme that aims to differentiate defect image patches of lithium-ion batteries in the first stage and to identify specific defect
View moreTargeting the issue that the traditional target detection method has a high missing rate of minor target defects in the lithium battery electrode defect detection, this paper proposes an improved
View moreIn this paper, a quality detection method for battery FPC (Flexible Printed Circuit) connectors based on active shape model template matching is proposed. It can deal with different kinds of connector appearance defects. Firstly, construct template data set of connector, acquire test images and apply cutting operation to original image, then execute tilt correction and
View moreTo address the challenges arising from the large scale and shape differences in defects on the end face of lithium battery casings, which make the identification of small target defects
View moreA battery without electrolyte is selected, and high voltages of several hundred volts are applied to the positive and negative electrodes. The test relies on tip discharge effect, where the batteries with burrs and metal foreign matters can be detected. By adjusting the test voltage, the detection rate for defects can reach up to 85%.
View moreCurrently, there are several methods for battery defect detection: (1) Dismantling the battery to inspect internal defects [148]. This method is costly and does not preserve the sample. Xie et al. [171] found that the battery aging process is accompanied by the appearance of lithium plating when using the Immersion Testing method,
View moreA python file with name "LithiumIonBatteryDefectDetection" contains, LiIonDefDet system to automatically detect the surface defect on Cylindrical Lithium Ion Battery. A folder with name
View moreThe 3D point cloud-based defect detection of lithium batteries used feature-based techniques to downscale the point clouds to reduce the computational cost, extracting the normals of the points and calculating their differences to detect the defects of the battery which assure the quality of the product.
View moreThe experimental results show that the mean average precision (mAP) value of the detection algorithm on the lithium battery validation dataset reaches 94% and the detection
View moreAutomotive 21700 series lithium batteries are prone to surface defects during production and transportation, thus affecting their performance, so we propose a full-surface defect detection method for battery cases based on the synthesis of traditional image processing and deep learning to address this problem.
Distribution of defects in the cylindrical battery case. To analyse the surface defect characteristics of a cylindrical battery case, most of the defects exist mainly on its cylindrical surface (side) and are affected by the material reflection problem, resulting in complex image acquisition and detection.
Currently, in industrial production, the majority of the quality inspection processes for battery cases are manual. However, workers have varying skill levels, and as working hours increase, ensuring accurate defect detection becomes more difficult, which can lead to occasional misdiagnosis and omissions.
Comparison of the detection models for defects on the side and bottom of the battery case. The performances of DSSD, Faster R-CNN, YOLOX, and YOLOv5 are poor in the detection of defects on cylindrical battery cases.
Among these, deformations, scratches, dents, and notches are considered serious defects that significantly impact the quality of the battery case, potentially affecting its performance and, in severe cases, leading to unforeseen accidents.
Since there is no publicly available defect dataset for cylindrical battery cases, a defect dataset is established, and the dataset is augmented and expanded via the traditional method and the ACGAN model.
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