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.
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...
There is not much literature about defect detection in Li-ion battery electrode and to the best of our knowledge this is the first work to apply deep learning to this problem.
1. We show that it is possible to accurately detect various types of defect in the complex microstructure of Li-ion battery from images of the electrodes using computer vision without the need for any hand-crafted feature extraction. 2.
Designing an EV battery fault detection algorithm that is implementable and effective for both EV manufacturers and owners needs to take practical social factors into account 30, 31, such as the data availability, economic trade-offs, sensor noise, and model privacy.
The use of pulse thermography for online quality control of Li-ion battery electrode was investigated in Sharp et al. (2014) and the effect of various electrode manufacturing defects on the electrochemical performance of Li-ion batteries was studied in Mohanty et al. (2016).
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 …
This work provides an overview of deep learning techniques for defect detection in microstructural images of Lithium-ion batteries obtained through light-optical …
Internal short circuit (ISC) is a critical cause for the dangerous thermal runaway of lithium-ion battery (LIB); thus, the accurate early-stage detection of the ISC failure is critical to improving the safety of electric …
Aiming to address the problems of uneven brightness and small defects of low contrast on the surface of lithium-ion battery electrode (LIBE) coatings, this study proposes a …
This work provides an overview of deep learning techniques for defect …
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 …
Rather than the noise information on the image, so as to improve the detection ability of lithium …
Full size image. 4 Results and ... A new on-line method for lithium plating detection in lithium-ion batteries. J. Power Sources 451 (2020) Google Scholar Download …
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 …
With the proliferation of Li-ion batteries in smart phones, safety is the main concern and an on-line detection of battery faults is much wanting. Internal short circuit is a …
To address the problem of miss- and false detection during quality inspection of lithium-ion battery cover screen printing (LBCSP), we propose a hybrid image re
In this work, we combine the A-scan and 2D/3D Total Focusing Method (TFM) …
4 · Specifically, in lithium battery shell defect detection, it achieves an mAP50 of 97.0%, representing a 4.6% improvement over Yolov8n. Its parameters and FLOPs are reduced by …
To our knowledge, this is the first attempt to apply deep learning to online SOH assessment of Li-ion battery. 10-year daily cycling data from implantable Li-ion cells are used to verify the …
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 …
The proposed method is suitable for the online real-time defect detection of LIBE coating defects in actual lithium-ion battery industrial production. Aiming to address the …
In this work, we combine the A-scan and 2D/3D Total Focusing Method (TFM) ultrasonic detecting technologies to in situ monitor and image the battery''s abnormal behavior …
Rather than the noise information on the image, so as to improve the detection ability of lithium battery surface defects. Experiments show that AIA DETR model can well detect the defect …
Deep learning computer vision methods were used to evaluate the quality of lithium-ion battery electrode for automated detection of microstructural defects from light …
This research addresses the critical challenge of classifying surface defects in lithium electronic …
Full size image. Our framework and datasets enable us to adopt and train the latest anomaly detection models. ... Q. et al. Fault diagnosis and abnormality detection of …
Zheng, Y. et al. Micro-short-circuit cell fault identification method for lithium-ion battery packs based on mutual information. IEEE Trans. Ind. Electron. 68, 4373–4381 (2020).
4 · Specifically, in lithium battery shell defect detection, it achieves an mAP50 of 97.0%, …