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

arXiv: https://arxiv.org/abs/2412.17105 · HTML: https://mlsanity.com/paper/2412.17105/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 18 references that are in the map.
- First posted on arXiv 22 Dec 2024.

## Builds on (most cited first, 5 of 5)

- [Deep High-Resolution Representation Learning for Human Pose Estimation](https://mlsanity.com/paper/1902.09212/) (2019)
- [SuperPoint: Self-Supervised Interest Point Detection and Description](https://mlsanity.com/paper/1712.07629/) (2017)
- [A Comprehensive Review of Image Analysis Methods for Microorganism Counting: From Classical Image Processing to Deep Learning Approaches](https://mlsanity.com/paper/2103.13625/) (2021)
- [Heatmap Regression via Randomized Rounding](https://mlsanity.com/paper/2009.00225/) (2020)
- [LOTR: Face Landmark Localization Using Localization Transformer](https://mlsanity.com/paper/2109.10057/) (2021)

## Abstract (arXiv)

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.