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Ellipse detection using the edges extracted by deep learning

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Abstract

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.

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Funding

Funding was provided by National Key Research and Development Program of China (Grant No. 2016YFE0206200) and National Natural Science Foundation of China (Grant Nos. U1613205, 51675291)

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Authors and Affiliations

  1. Department of Mechanical Engineering, Tsinghua University, Beijing, 100084, China

    Chicheng Liu, Rui Chen, Ken Chen & Jing Xu

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  1. Chicheng Liu
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  2. Rui Chen
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  3. Ken Chen
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  4. Jing Xu
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Correspondence to Jing Xu.

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Liu, C., Chen, R., Chen, K. et al. Ellipse detection using the edges extracted by deep learning. Machine Vision and Applications 33, 63 (2022). https://doi.org/10.1007/s00138-022-01319-5

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