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Efficient and Precise Ellipse Detection via Deep Learning-Based Axis-Aligned Bounding Box Estimation
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AI
DOI：10.1587/transinf.2025EDL8032.png)
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Abstract
En 中文
Ellipse detection plays a critical role in fields such as medical diagnosis, environmental monitoring, and industrial automation. However, traditional methods (e.g., Hough transform, least-squares fitting, and edge-following techniques) suffer from high computational complexity and poor noise robustness. To address these limitations, we propose a hybrid framework that integrates deep learning with geometric constraints. First, Faster RCNN is employed to localize axis-aligned bounding boxes (AABBs) of ellipses. Then, a point-pair filtering strategy extracts edge points satisfying predefined geometric constraints, followed by weighted least-squares fitting to estimate ellipse parameters. Compared with traditional approaches, our method directly identifies AABBs, significantly enhancing both the efficiency and accuracy of multi-target ellipse detection in practice. Experiments are conducted on two synthetic datasets. The results show that our proposed method achieves superior precision and F-measure compared to conventional ellipse detection algorithms.
Keywords:
ellipse detection
least squares fitting
edge-following methods
computer vision
Journal
I
IF:
0.8
Papers:
171
Citations:
2.3K