Hybrid Multi-Stage Learning Framework for Edge Detection: A Survey

TL;DR

The paper tackles robust edge detection under challenging real-world conditions by proposing a Hybrid Multi-Stage Learning Framework that decouples feature extraction (CNN) from classification (SVM). By combining encoder–decoder CNN features with pixel-wise SVM classification, the approach achieves superior edge localization and perceptual coherence, validated on BSDS500 and NYUDv2 where ODS and OIS surpass traditional and several learning-based detectors, while AP remains competitive. The modular, interpretable design aims to bridge classical and deep learning paradigms, offering scalability suitable for edge deployments. Overall, the method demonstrates that integrating traditional ML classifiers with deep feature learning can yield high-quality, robust edge maps with practical deployment advantages.

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.

Figures (6)

• Figure 1: Proposed edge detection architecture.
• Figure 2: Training progress of Our proposed solution.
• Figure 3: Visual comparison between baseline edge detection methods and Our proposed solution using NYUD_v2 arbelaez2010contour dataset.
• Figure 4: Visual comparison between baseline edge detection methods and Our proposed solution using BSDS500 martin2001database dataset.
• Figure 5: More on visual comparison between baseline edge detection methods and Our proposed solution using BSDS500 martin2001database dataset.