用梯度点优化卷积网络热图回归，提升电池电极定位精度。
Refining CNN-based Heatmap Regression with Gradient-based Corner Points for Electrode Localization
- 结合像素梯度与热图回归，定位关键点
- 利用角点先验修正下采样带来的误差，定位更准
- 适合电池缺陷检测与工业质检场景
我们提出一种锂离子电池电极位置检测方法。首先通过角点检测定位电池X射线图像的感兴趣区域（ROI），然后使用卷积神经网络在该区域内回归极片位置。最后，利用角点先验对回归结果进行优化和修正，显著缓解了训练过程中特征图下采样和填充导致的定位精度损失。实验表明，将传统像素梯度分析与基于CNN的热图回归相结合，可有效提升关键点提取的准确性和效率，实现显著性能提升。
原文摘要 · Abstract (English)
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.
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