What is lithium battery electrode defect detection?

In lithium battery electrode defect detection, the traditional defect detection algorithm makes it difficult to meet the defect detection task of the high-speed moving electrode in the industrial production environment. The faults on the lithium battery electrode are minor and complex, with many defects.

Can yolov8 improve battery electrode defect detection?

Multiple requests from the same IP address are counted as one view. Targeting 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 and optimized battery electrode defect detection model based on YOLOv8.

Why is early detection of electrode defects important?

Therefore, monitoring of production process and early detection of electrode defects are especially important as the basis for developing reliable, high quality batteries and to minimize the cell rejection rate after fabrication and testing (Mohanty et al. 2016).

Can deep learning solve a defect detection problem in Li-ion battery electrode?

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.

Can deep learning computer vision detect microstructural defects in lithium-ion battery electrodes?

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.

How accurate is the yolov8-gce battery pole chip defect detection model?

According to the experimental findings, the mAP of the YOLOv8-GCE battery pole chip defect detection model in the self-built data set reaches 97.2%, and the FPS is maintained at 43f·s −1. In contrast to the current model, the method has higher detection accuracy and reduces the requirement for platform computing power.