Model-aware ellipse detection via parametric correlation learning
Qi Jia, Z. Liu, Yu Liu, Yi Wang, Xinwei Xue, Weimin Wang · Signal Processing · 2025
Ellipse detection presents a significant challenge in computer vision and pattern recognition, often hindered by traditional parameter regression methods that fail to account for the unique geometric characteristics and complex parameter interactions of ellipses. These limitations frequently result in imprecise detections, notably with small or partially occluded ellipses. To overcome these challenges, we propose EDNet, a novel ellipse detection network that exploits the geometric properties of ellipses, thus moving beyond the reliance on internal textures. EDNet improves ellipse detection by refining the loss function to better capture the relationship between the error and each parameters during training. It features a LoG-like Edge Detection Module (LEDM) and an Edge Guided Module (EGM) for precise boundary extraction and multi-scale feature enhancement. Additionally, an auxiliary component estimates ellipse vertices, boosting accuracy for occluded ellipses. Experimental results on two wildly-used benchmark datasets demonstrate that EDNet achieves significant improvements, with an average detection accuracy increase of 6% and 10% over leading state-of-the-art models. • We concentrate on the geometric characteristics of ellipse detection via Edge Detection Module and Edge Guided Module. • We design an auxiliary head for the estimation of four ellipse vertices, invoking additional feature attention on these pivotal points. • We establish the relations between the error and geometric characteristics of the ellipse by a model-aware loss function.