Defective Edge Detection Using Cascaded Ensemble Canny Operator

TL;DR

This paper introduces a Cascaded Ensemble Canny operator that improves edge detection accuracy and refinement in complex scenes, outperforming existing ensemble and traditional methods on challenging datasets.

Contribution

The paper proposes a novel cascaded ensemble approach to enhance edge detection accuracy and edge refinement over existing ensemble and traditional methods.

Findings

Outperforms existing edge detection networks on challenging datasets

Provides more accurate and refined edge maps in complex scenes

Demonstrates superior performance in both metrics and output quality

Abstract

Edge detection has been one of the most difficult challenges in computer vision because of the difficulty in identifying the borders and edges from the real-world images including objects of varying kinds and sizes. Methods based on ensemble learning, which use a combination of backbones and attention modules, outperformed more conventional approaches, such as Sobel and Canny edge detection. Nevertheless, these algorithms are still challenged when faced with complicated scene photos. In addition, the identified edges utilizing the current methods are not refined and often include incorrect edges. In this work, we used a Cascaded Ensemble Canny operator to solve these problems and detect the object edges. The most difficult Fresh and Rotten and Berkeley datasets are used to test the suggested approach in Python. In terms of performance metrics and output picture quality, the acquired results outperform the specified edge detection networks