Abstract
Camera calibration plays an important role in various optical measurement and computer vision applications. Accurate calibration parameters of a camera can give a better performance. The key step to camera calibration is to robustly detect feature points (typically in the form of checkerboard corners) in the images captured by the camera. This paper proposes a robust checkerboard corner detection method for camera calibration based on improved YOLOX deep learning network and Harris algorithm. To get high checkerboard corner detection robustness against the images with poor quality (i.e., degradation, including focal blur, heavy noise, extreme poses, and large lens distortions), we first detect the corner candidate areas through the improved YOLOX network which attention mechanism is added. Then, the Harris algorithm is performed on these areas to detect sub-pixel corner points. The proposed method is not only more accurate than the existing methods, but also robust against the types of degradation. The experimental results on different datasets demonstrate its superior robustness, accuracy, and wide effectiveness.
Highlights
Camera calibration plays an important role in various optical measurement and computer vision applications
We propose a robust checkerboard corner detection method for camera calibration based on improved YOLOX deep learning network and the Harris algorithm
To solve the shortcomings of traditional corner detection methods, we propose a robust corner detection method based on the improved YOLOX deep learning network and the Harris algorithm
Summary
Camera calibration plays an important role in various optical measurement and computer vision applications. Accurate calibration parameters of cameras can perform better in scene reconstruction, stereo matching, and other aspects in the field of computer vision and optical measurement. The relationship can be represented by feature points, such as checkerboard corners, circular array, and so on. As a conventional feature extraction template, checkerboard corners detection is widely applied in most research. Research in corner detection is mainly divided into three methods according to the definition of corners: intensity-based, contour-based, and binary-based methods [3]. Within these methods, the binarybased method is not popular in actual engineering applications
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