This systematic review aims to explore and synthesize the existing literature on defect detection methods in lithium batteries. With the increasing demand for reliable and efficient ...
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 cells, demonstrating that deep learning models are able to learn accurate representations of the microstructure images well enough to distinguish …
Abstract: This research addresses the critical challenge of classifying surface defects in lithium electronic components, crucial for ensuring the reliability and safety of lithium …
Battery defect detection based on the abnormality of external parameters is a promising way to reduce this kind of thermal runaway accidents and …
The findings in this study indicated that it is important to detect defects that occur in battery manufacturing, and defects greatly affect battery performance, as …
AI Visual Inspection: Defect Detection in Manufacturing
Quality assurance of blister packages for defect detection is necessary, but manual inspection is inefficient. The proposed methodology develops an automated defect detection system using the YOLOv7 to detect five defect categories: broken pill, crack pill, empty pill, foreign object, and color mismatch. The proposed model is initially trained for …
8 Z.U.Rehmanetal. is then used to create a 3D point cloud, which is a digital representation of the battery''s surface made up of numerous individual points that collectively form a 3D model. Our dataset includes information on different types of defects, such as
Fault detection of the electric vehicle battery system is vital for safe driving, energy economy, and lifetime extension. This paper proposes a data-driven method to achieve early and accurate battery system fault detection to realize rapid early warning. The method first adopts the support vector data description model mapping the feature of …
Hundreds of electric vehicle (EV) battery thermal runaway accidents resulting from untreated defects restrict further development of EV industry. Battery defect detection based on the abnormality of external parameters is a promising way to reduce this kind of thermal runaway accidents and protect EV consumers from fire danger. However, …
"In the past, detecting a 500-micron defect was the goal; now it is a 50-micron defect. And with batteries integral to increasingly important products like electric vehicles and battery energy storage systems, they want to inspect every item, not just a …
Object detection models can be divided into one-stage detection models and two-stage detection models, which differ in terms of their accuracy and speed. RetinaNet (Lin et al., 2017b), as the first one-stage algorithm to achieve higher accuracy than two-stage detection algorithms, is widely used in various detection tasks due to its …
In this paper, we propose a defect detection system for PV panels based on an improved DenseNet neural network. The system model dataset is first established by dividing a large number of PV panel images into Ho image pre-processing to improve the training effect of the neural network.
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 …
DOI: 10.1784/insi.2024.66.5.305 Corpus ID: 269679222 Resolving data imbalance in alkaline battery defect detection: a voting-based deep learning approach @article{Xu2024ResolvingDI, title={Resolving data imbalance in alkaline battery defect detection: a voting-based deep learning approach}, author={Zhenying Xu and Bangguo …
Surface defect detection of industrial components based on ...
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