.png){kind=icon}
Return
An Improved Deep Learning Network Based Defect Detection Algorithm for Lithium-ion Battery Pole Chip
.png){kind=icon}
PRE
AI
DOI：10.1109/ICCCBDA56900.2023.10154639.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
In order to meet the needs of the detection accuracy and speed of lithium-ion battery chip defects, this article proposes an improved algorithm based on deep learning YOLO5. First,introduce the coordinate attention mechanism (CA) to strengthen the study of the characteristics of the image area of the lithium-ion polar tablet so that the model focuses on the extraction of the characteristics of the polar defects and weakens the effects of complex background on the test results. Further, improve the accuracy of polar defect detection, and use the CIoU loss function to replace the GIoU function, so that the regression process focuses more on high-quality anchor frames and improves the convergence speed, regression accuracy, and robustness of the model. Based on the above experimental results, the two improvements were jointly added to the YOLOv5 model, the mAP was increased by 1.48%, and the detection speed FPS was increased by 2 f/s-1.The YOLOv5-CC algorithm proposed in this article is a lithium-ion with both detection accuracy and practicality.Battery pole defect detection algorithm has high application value.
Keywords:
lithium-ion battery
defect detection
attention mechanism
loss function
yolo5
Journal
I
IF:
0
Papers:
4
Citations:
0
Organization
Cited Papers
Surface Defects Detection and Identification of Lithium Battery Pole Piece Based on Multi-Feature Fusion and PSO-SVM.png)
IEEE ACCESS
OAAI
IEEE ACCESSIF3.6
OAAIShareSaveShareSaveThermal comfort, perceived air quality, and cognitive performance when personally controlled air movement is used by tropically acclimatized personsIndoor AirOAAI
Indoor AirIF0
OAAIShareSaveResearch progress, challenges and prospects of fault diagnosis on battery system of electric vehiclesAPPLIED ENERGY
APPLIED ENERGYIF11
PREAI
ShareSave
DCM-CNN: Densely Connected Multiloss Convolutional Neural Networks for Light Field View SynthesisIEEE ACCESSOAAI
IEEE ACCESSIF3.6
OAAIShareSaveShareSave
View more