融合CNN与SVM的分阶段框架，提升边缘检测精度与可解释性。
Hybrid Multi-Stage Learning Framework for Edge Detection: A Survey
- 分阶段处理：先用CNN提取特征，再用SVM分类，分离表示与决策。
- 在BSDS500和NYUDv2上，ODS和OIS均优于传统方法与最新学习模型。
- 适合需要高可解释性、抗噪强的工业边缘检测场景。
边缘检测是计算机视觉中的基础但极具挑战性的任务，尤其在光照变化、噪声干扰和复杂场景下表现困难。本文提出一种混合多阶段学习框架，将卷积神经网络（CNN）的特征提取能力与支持向量机（SVM）分类器结合，以提升边缘定位与结构准确性。与传统端到端深度学习模型不同，该方法解耦特征表示与分类阶段，增强鲁棒性与可解释性。在BSDS500和NYUDv2等基准数据集上的大量实验表明，所提框架在最优数据集尺度（ODS）和最优图像尺度（OIS）指标上均超越传统边缘检测器及近期学习方法，同时保持了具有竞争力的平均精度（AP）。定性和定量结果均显示，该方法在边缘连续性、噪声抑制与感知清晰度方面均有显著提升。本工作不仅连接了经典方法与深度学习范式，也为构建可扩展、可解释且高质量的边缘检测解决方案开辟了新方向。
原文摘要 · Abstract (English)
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.
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