Efficient and Precise Ellipse Detection via Deep Learning-Based Axis-Aligned Bounding Box Estimation

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.