Sub-Pixel Checkerboard Corner Localization for Robust Vision Measurement

Precise localization of checkerboard corner is crucial for camera calibration and accurate vision measurement. The accuracy of classical corner localization methods is affected by environmental factors, which hinders the effectiveness of vision measurements in complex scenes. We propose a novel end-to-end sub-pixel method for localizing checkerboard corners to overcome this challenge, demonstrating improved robustness in adverse conditions. We employ an EfficientNetv2-like backbone network to map the input image to a set of corners using offset regression. The continuous heatmap matching loss enables fine-grained spatial learning ability, while score loss ensures prediction sparsity and suppresses false positive probabilities. The model is trained on synthesized datasets with precise corner labels. Quantitative experimental results demonstrate the superior sub-pixel localization precision and robustness of our method on both synthetic and real-world datasets.