| Title |
High-precision Ellipse Detection Based on Edge Feature Learning |
| Authors |
(Yuhao Lv) ; (Hancheng Yu) ; (Mingkui Feng) ; (Ming Zhou) |
| DOI |
https://doi.org/10.5573/IEIESPC.2026.15.5.599 |
| Keywords |
Rotated object detection; Ellipse detection; Edge learning; Parameter regression |
| Abstract |
Ellipse detection holds significant importance in image processing and plays a key role across various application scenarios. However, achieving accurate and robust ellipse detection remains challenging. Traditional methods can perform precise detection in controlled environments, but they often struggle with ellipses in complex scenes. Conversely, existing deep learning-based approaches demonstrate improved robustness in handling complex scenarios; however, they frequently exhibit limitations in detection accuracy. To address these challenges, this paper presents a novel auxiliary branch based on edge extraction to enhance feature learning and parameter regression for ellipses, ultimately achieving high-precision detection. The auxiliary branch consists of two fundamental components: an edge extraction module and a rotated region of interest (RoI) parameter regression module. The edge extraction module leverages a U-Net architecture to effectively capture and refine elliptical edge features, enabling precise learning of target contours. Simultaneously, the rotated RoI parameter regression module integrates these edge features with preliminary detection results from the first stage to perform accurate parameter regression, thereby improving localization performance. Experimental results demonstrate that the proposed method significantly enhances detection performance, particularly under high overlap ratios. |