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.

翻译：为了改进检查板角落对质量差的图像的检测力度,例如镜像扭曲、极端立方和噪音,我们提议一种新的检测算法,这种算法可以在不事先了解检查板模式的情况下,在多种假设情况下保持对投入的高度准确性。整个算法包括一个检查板角探测网络和一些后处理技术。网络模型是一个完全渐进的网络,其损失功能和学习率得到改进,它可以通过高效的推断和学习处理任意大小的图像,产生相应尺寸的像素角分的输出。此外,为了消除假的阳性,我们采用了三种后处理技术,包括与最大反应、非最大抑制和集群有关的阈值。对两个不同的数据集的评价显示其高度稳健性、准确性和在与最新方法的定量比较中的广泛适用性,例如MATE、CESS、ROCSADE和OCAMCalib。