Defective Edge Detection Using Cascaded Ensemble Canny Operator
This work addresses edge detection challenges in computer vision for applications like image analysis, but it appears incremental as it builds on ensemble learning methods.
The paper tackled the problem of inaccurate edge detection in complex scenes by proposing a Cascaded Ensemble Canny operator, which outperformed existing methods on the Fresh and Rotten and Berkeley datasets in terms of performance metrics and output image quality.
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