Existing edge detection methods are based on fixed logics, which are not intelligent enough to distinguish useful edges and useless/noise edges. Recent ellipse detection methods developed some excellent algorithms that can still detect ellipses, while a large number of noise edges exist. However, these algorithms are compromised that will lose some precision and recall. This paper proposes a deep learning model that can intelligently distinguish useful edges and useless edges. Therefore, high-quality edge maps with low noise can be obtained. An arc-growing-based ellipse detection method is also proposed to take full advantage of the high-quality edge maps. Experiments are performed to reveal the mechanism of the deep learning model and to verify the performance of the proposed method. The experimental results demonstrate that the proposed method performs far better than the state-of-the-art in terms of precision, recall and the F-measure on industrial images and performs slightly better on natural images.

现有的边缘检测方法是基于固定逻辑的，其智能性不足以区分有用边缘和无用/噪声边缘。最近的椭圆检测方法开发了一些优秀的算法，这些算法仍然可以检测到椭圆，同时存在大量的噪声边缘。但是，这些算法会受到损害，会损失一些精度和召回率。本文提出了一种深度学习模型，可以智能地区分有用边和无用边。因此，可以获得低噪声的高质量边缘图。还提出了一种基于弧生长的椭圆检测方法，以充分利用高质量的边缘图。进行实验以揭示深度学习模型的机制并验证所提出方法的性能。