A highly accurate and robust deep checkerboard corner detector

Abstract

Abstract Checkerboard corners are routinely used during tasks such as camera calibration, and accurate detection of them is essential. Traditional methods such as those based on the Harris corner detector are usually affected by image artefacts such as noise and blur. The more recently proposed deep network approach also suffers from the lack of accurate training data. In this article, we make the following contributions: First, we propose a synthetic training data generation method that simulates the real imaging process, with the most notable being that the exact sub‐pixel corner positions in the image become readily available. Second, we design a relatively simple deep network and train it using the synthetic data generated by the proposed method. Finally, because the exact sub‐pixel corner positions can be obtained in the proposed method, this article paves a way for objectively comparing different checkerboard corner detectors in terms of metrics such as the mean Euclidean location error. Experimental results using both synthetic and real data show that the proposed detector significantly outperforms typical methods, including the commonly used Matlab camera calibration toolbox, the OpenCV checkerboard corner detectors, and the more recently proposed deep learning‐based methods.

Keywords

• Image recognition
• Image sensors
• Computer vision and image processing techniques
• Neural nets