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：为提升棋盘格角点检测在镜头畸变、极端位姿及噪声等低质量图像中的鲁棒性，本文提出一种无需任何棋盘格先验知识、即可在多场景输入下保持高精度的新型检测算法。该算法包含棋盘格角点检测网络及若干后处理技术。网络模型采用全卷积网络结构，通过改进损失函数与学习率策略，能处理任意尺寸图像，并基于高效推理与学习生成对应尺寸的逐像素角点置信度输出。为消除误检，我们采用三种后处理技术：基于最大响应的阈值筛选、非极大值抑制及聚类分析。在两个不同数据集上的定量对比实验表明，该方法相较于MATE、ChESS、ROCHADE及OCamCalib等前沿方法，展现出更优的鲁棒性、精度与广泛适用性。