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Aiming to improve the checkerboard corner detection robustness against theimages with poor quality, such as lens distortion, extreme poses, and noise, wepropose a novel detection algorithm which can maintain high accuracy on inputsunder multiply scenarios without any prior knowledge of the checkerboardpattern. This whole algorithm includes a checkerboard corner detection networkand some post-processing techniques. The network model is a fully convolutionalnetwork with improvements of loss function and learning rate, which can dealwith the images of arbitrary size and produce correspondingly-sized output witha corner score on each pixel by efficient inference and learning. Besides, inorder to remove the false positives, we employ three post-processing techniquesincluding threshold related to maximum response, non-maximum suppression, andclustering. Evaluations on two different datasets show its superior robustness,accuracy and wide applicability in quantitative comparisons with thestate-of-the-art methods, like MATE, ChESS, ROCHADE and OCamCalib.

CCDN: Checkerboard Corner Detection Network for Robust Camera Calibration