Ellipses are important elements in projective ge-
ometry. Accurate extraction of ellipse information is the first
step in many computer vision applications, such as ellipse-based
camera calibration and camera pose estimation. At present, most
ellipse detection algorithms rely on the edge features extracted
by Canny, which leads to a number of wrong detection results
since non-ellipse edges features are also involved. To address this
problem, this paper proposes a novel ellipse marker detection
neural network, called EllipseNet. Notably, we propose a new loss
function to enhance the rotation perception ability of EllipseNet.
Furthermore, a novel ellipse marker data enhancement method is
proposed for saving the time cost of labelling ellipse parameters.
Experiments show that EllipseNet can improve the detection
precision of ellipse regions by more than 3% improvement
compared with other SOTA general object detectors