Hybrid Multi-Stage Learning Framework for Edge Detection: A Survey
This work addresses the problem of robust and interpretable edge detection for computer vision applications, representing an incremental improvement by combining classical and deep learning approaches.
The paper tackles the challenge of edge detection in computer vision under varying conditions by introducing a Hybrid Multi-Stage Learning Framework that integrates CNN feature extraction with an SVM classifier, resulting in improved edge localization and structural accuracy, as demonstrated by outperforming traditional and recent methods on benchmarks like BSDS500 and NYUDv2 in terms of ODS and OIS metrics.
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.