Abstract
Edge detection remains a fundamental yet challenging task in computer vision, especially under varying illumination, noise, and complex scene conditions. This paper introduces a Hybrid Multi-Stage Learning Framework that integrates Convolutional Neural Network (CNN) feature extraction with a Support Vector Machine (SVM) classifier to improve edge localization and structural accuracy. Unlike conventional end-to-end deep learning models, our approach decouples feature representation and classification stages, enhancing robustness and interpretability. Extensive experiments conducted on benchmark datasets such as BSDS500 and NYUDv2 demonstrate that the proposed framework outperforms traditional edge detectors and even recent learning-based methods in terms of Optimal Dataset Scale (ODS) and Optimal Image Scale (OIS), while maintaining competitive Average Precision (AP). Both qualitative and quantitative results highlight enhanced performance on edge continuity, noise suppression, and perceptual clarity achieved by our method. This work not only bridges classical and deep learning paradigms but also sets a new direction for scalable, interpretable, and high-quality edge detection solutions.
Abstract (translated)
边缘检测仍然是计算机视觉中的一个基本且具有挑战性的任务，尤其是在不同的照明条件、噪声以及复杂场景下。本文提出了一种混合多阶段学习框架，该框架结合了卷积神经网络（CNN）的特征提取与支持向量机（SVM）分类器的功能，以提高边缘定位和结构准确性。不同于传统的端到端深度学习模型，我们的方法解耦了特征表示和分类阶段，从而增强了鲁棒性和可解释性。 在BSDS500和NYUDv2等基准数据集上进行的大量实验表明，所提出的框架在最优数据规模（ODS）和最优图像尺度（OIS）方面超过了传统边缘检测器以及近期的学习方法，并且保持了竞争性的平均精度（AP）。无论是定性还是定量结果都显示出了我们的方法在边缘连续性、噪声抑制及感知清晰度方面的性能提升。 这项工作不仅连接了经典学习与深度学习范式，而且还为可扩展的、解释性强和高质量的边缘检测解决方案开辟了一条新的道路。
URL
https://arxiv.org/abs/2503.21827
