Automatic Checkerboard Detection for Robust Camera Calibration

Accurate checkerboard recognition is vital for basic camera calibration in many machine vision tasks. However, existing detectors usually encounter two problems. First, X-corner location precision is easily affected by undesired factors such as noise and distortion. Then, the pattern recovery requires manual input of the dimensions of the real corner matrix, and most recovery schemes can’t deal well with the pattern with occlusion or missing corners. In this paper we propose a novel CNN-based checkerboard detection framework to address these problems. This framework consists of three sub-modules: 1) an X-corner detection network to identify as many real corners as possible. 2) a sub-pixel refinement technique obtained from the geometric analysis of the image response map and grayscale to find precise corner locations. 3) a pattern recovery scheme to find the regular checkerboard layout even with high distortion and partial occlusion. Quantitative experimental results show that the proposed approach has higher accuracy and stronger robustness than state-of-the-art methods to both synthetic images and real-world camera calibration scenarios.