Refining CNN-based Heatmap Regression with Gradient-based Corner Points for Electrode Localization

TL;DR

This paper introduces a novel method combining corner point detection and CNN-based heatmap regression to accurately localize electrodes in lithium-ion batteries, improving robustness against common CNN training distortions.

Contribution

It presents a new approach that integrates gradient-based corner detection with CNN heatmap regression, enhancing electrode localization accuracy in battery imaging.

Findings

Significant improvement in localization accuracy.

Enhanced robustness against CNN training distortions.

Better efficiency in electrode detection process.

Abstract

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.