CCDN: Checkerboard Corner Detection Network for Robust Camera Calibration
This work addresses camera calibration robustness for computer vision applications, presenting an incremental improvement with a novel detection algorithm.
The paper tackled the problem of robust checkerboard corner detection for camera calibration under challenging conditions like lens distortion and noise, achieving superior robustness and accuracy compared to state-of-the-art methods in evaluations on two datasets.
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.