Refining CNN-based Heatmap Regression with Gradient-based Corner Points for Electrode Localization
This is an incremental improvement for battery manufacturing or inspection, addressing localization accuracy loss from network operations.
The paper tackled electrode localization in lithium-ion batteries by combining CNN-based heatmap regression with gradient-based corner point detection, resulting in significant performance improvements in accuracy and efficiency.
We propose a method for detecting the electrode positions in lithium-ion batteries. The process begins by identifying the region of interest (ROI) in the battery's X-ray image through corner point detection. A convolutional neural network is then used to regress the pole positions within this ROI. Finally, the regressed positions are optimized and corrected using corner point priors, significantly mitigating the loss of localization accuracy caused by operations such as feature map down-sampling and padding during network training. Our findings show that combining traditional pixel gradient analysis with CNN-based heatmap regression for keypoint extraction enhances both accuracy and efficiency, resulting in significant performance improvements.