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