A YOLO-based Real-time Packaging Defect Detection System
4. Real-time Packaging Defect Detection System 4.1. The proposed system for detecting packaging defects in real-time In this section, we present the architecture, components, and functions of the YOLO-based real-time packaging defect detection system. The architecture consists of four main components and as shown in Figure 2.
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Image-based defect detection in lithium
Machine vision systems for automatic defect detection commonly adopt 2D image-based systems or 3D laser triangulation systems. 2D and 3D systems present opposite
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Battery defect detection for real world vehicles based on
A significant amount of research has been conducted on fault diagnosis for battery systems. There are three main categories of fault diagnosis methods: knowledge-based methods, model-based methods, and data-driven methods. the current power battery defect detection is mostly based on equipment testing after production and recall, which does
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Deep learning based 3D defect detection system using
Part inspection machines of industrial manufacturing systems are being newly evolved as intelligent machines with the technology innovation of artificial intelligence. Especially, the automation of defect detection systems in the field of casting industry has been widely studied, applying deep learning based inspection algorithms due to its inspection difficulties with 2D
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HE-Yolov8n: an innovative and efficient method for detecting
Experimental results demonstrate that HE-Yolov8n significantly outperforms mainstream models in detecting surface defects. Specifically, in lithium battery shell defect
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Autonomous Visual Detection of Defects from Battery
The process of defect detection is divided into three steps: 1)data collection, i.e.,collectingthe electrode images that include agglomerates, bubbles, foil, and scratches, 2) image annotation,
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Diagnostics for defect detection in electric vehicles''
Diagnostics for defect detection in electric vehicles'' battery systems. Save page. Share + 28 Nov 2023 Blog. The Battery Management System (BMS) has a number of tasks, including ensuring that the battery cells
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(PDF) Design and Implementation of Defect Detection
Thus, the defect rate of secondary battery lead taps is reduced, productivity is improved, and companies can gain a competitive advantage. Processes 2023, 11, 2751 3 of 16
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Research on detection algorithm of lithium battery surface defects
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
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Recent advances in model-based fault diagnosis for lithium-ion
In particular, we offer (1) a thorough elucidation of a general state–space representation for a faulty battery model, involving the detailed formulation of the battery
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A review of automated solar photovoltaic defect detection systems
battery bank, degrading the PV module''s efficiency [3,4]. Moreover, the new generations of solar cells, such as Copper-indium-Gallium-disulfide defect detection in PV systems, which are categorised in this article into Imaging-Based Techniques (IBTs)
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Design and Development of a Precision Defect Detection System
To detect defects on PCBs, the system gathers extensive images of both flawless and defective products to train a deep learning model. An AI engine generated through this deep learning process is then applied to conduct defect inspections. The developed high-speed defect detection system was evaluated to have an accuracy of 99.5% in the experiment.
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State of the Art in Defect Detection Based on Machine
Structured light illumination technology is widely used in visual measurement and inspection. Based on laser structured light vision, Li et al. [] developed an inspection system for weld bead profile monitoring, measuring,
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Online vision system for battery FPC connector defect detection
In 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
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Machine vision-based detection of surface defects in cylindrical
Semantic Scholar extracted view of "Machine vision-based detection of surface defects in cylindrical battery cases" by Yuxi Xie et al., title={Machine vision-based detection of surface defects in cylindrical battery cases}, author={Yuxi Xie and Xiang Xu and ShiYan Liu}, journal={Journal of Energy Storage}, year={2024}, url={https://api
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(PDF) Coating Defects of Lithium-Ion
A widely used inline system for defect detection is an optical detection system based on line scan cameras and specialized lighting. The cameras scan the electrode, and
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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 approach to
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An end-to-end Lithium Battery Defect Detection Method Based on
Experiments show that AIA DETR model can well detect the defect target of lithium battery, effectively reduce the missed detection problem, and reach 81.9% AP in the lithium battery
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Deep-Learning-Based Lithium Battery Defect Detection via Cross
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
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Defects in Lithium-Ion Batteries: From Origins to Safety Risks
They have three advantages: (1) non-destructive testing: it allows for internal inspection of batteries without damaging them, (2) cost efficiency: ultrasonic technology provides a cost-effective option for defect detection, (3) Visualization: it enables visualization of internal defects through ultrasonic scanning, giving a clearer understanding of the battery''s internal
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DCS-YOLO: Defect detection model for new energy vehicle battery
detection algorithms in the manufacturing scenario. Various methods for detecting battery defects have been proposed and implemented in battery manufacturing. Presently, machine vision defect detection methods are primarily categorized into two types: 1. Traditional methods combine image processing with machine learning to detect features
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Design of defect detection system for process parts based on
This study addresses the problem of surface defects in parts produced on traditional production lines and designs a process defect detection system based on machine vision. Firstly, taking the detection of production defects in seat components as an illustrative example, we select appropriate imaging equipment and construct imaging platforms
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Defect detection system based on deep learning optimization
This system combines machine vision and deep learning, and deeply investigates key technologies such as vision sensing, platform control, camera calibration, image processing, human-computer interaction technology, and cloud database, and designs and realizes the combination of deep learning and traditional vision inspection system. The traditional vision
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Battery defect detection for real world vehicles based on Gaussian
Highlights • A SOC interval extraction method is proposed to improve the defect detection accuracy and reduce computation time. • Threshold parameterization of DLCSS
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A Real-Time Automated Defect Detection
The automated defect detection system for ceramic pieces operates in real time and achieves impressive performance results. It has a testing accuracy of 98.00% and
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(PDF) A Systematic Review of Lithium Battery Defect Detection
This review categorizes and evaluates different detection techniques, including electrochemical, non-destructive testing (NDT), electrical, acoustic emission, optical methods,
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An end-to-end Lithium Battery Defect Detection Method Based
DOI: 10.1109/ICMSP58539.2023.10170926 Corpus ID: 259835564; An end-to-end Lithium Battery Defect Detection Method Based on Detection Transformer @article{Yang2023AnEL, title={An end-to-end Lithium Battery Defect Detection Method Based on Detection Transformer}, author={Kun Yang and Lixin Zheng}, journal={2023 5th International Conference on Intelligent
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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% and the detection
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Surface Defects Detection and Identification of Lithium Battery
In order to realize the automatic detection of surface defects of lithium battery pole piece, a method for detection and identification of surface defects of lithium battery pole piece based on multi-feature fusion and PSO-SVM was proposed in this paper. Firstly, image subtraction and contrast adjustment were used to preprocess the defect image to weaken the
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Detecting and Modeling Defect Structures in Battery Cells
Early detection of defect structures means: Shorter time to discovery of cause Faster insights into cell materials and designs Early detection of defect structures means: Cells can be pulled out of the process flow sooner • Keysight Battery Test System: +/-
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Defect Detection in Lithium-Ion Batteries Using Non-destructive
The application of ultrasonics in lithium-ion battery defect detection is a testament to the versatility and effectiveness of this technology. Its capability to offer detailed, non-destructive insights into the interior structure of the battery''s makes it an invaluable tool for ensuring quality, safety, and performance.
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Deep Learning-Based Defect Detection System Combining
Automated defect detection is an important part of manufacturing, where deep learning-based detection methods are widely used. However, these methods are often limited by the defective features in 2D images, and it is difficult to obtain significant defect features under single illumination, especially for metal parts.
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Case Study: AI Vision Defect Detection System for Battery
This system integrates cameras and measurement tools into the vision system to inspect products in real-time and identify defects, achieving groundbreaking improvements in defect detection and
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3D Point Cloud-Based Lithium Battery Surface Defects Detection
The 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 more6 FAQs about [Yemen Battery Defect Detection System]
Can a full-surface defect detection method be used for automotive 21700 series lithium batteries?
Automotive 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.
Can a 3D visual measurement system detect lithium battery surface defects?
A 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 address the problem of lithium battery surface defect detection based on region growing proposal algorithm.
Can AIA DETR model detect lithium battery defect?
Experiments show that AIA DETR model can well detect the defect target of lithium battery, effectively reduce the missed detection problem, and reach 81.9% AP in the lithium battery defect data set Conferences > 2023 5th International Confer...
How to detect lithium battery surface defects?
Detecting the lithium battery surface defects is a difficult task due to the illumination reflection from the surface. To overcome the issue related to labeling and training big data by using 2D techniques, a 3D point cloud-based technique has been proposed in this paper.
How to detect defects in lithium batteries with aluminum/steel shells?
However, detecting defects in lithium batteries with aluminum/steel shells is challenging due to the reflective surface and limitations of 2D computer vision detection methods . To overcome issues with deformation and occlusion characteristics, literature has devised a method that uses adversarial networks and spatial dropout networks.
Can bounding boxes be used to detect defects in lithium batteries?
The use of bounding boxes is a valuable technique for the characterization and analysis of defects in lithium batteries and can provide insights for the development of enhanced battery technologies. In this work, we presented a framework for defect detection on lithium battery surfaces based on the characterization of the point cloud data.
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