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

翻译：边缘检测一直是计算机视觉领域最具挑战性的任务之一，这源于从包含各类尺寸物体的真实世界图像中准确识别边界与边缘的固有困难。基于集成学习的方法通过结合多种骨干网络与注意力模块，在性能上超越了Sobel和Canny边缘检测等传统方法。然而，当面对复杂场景图像时，这些算法仍面临显著挑战。此外，现有方法所识别的边缘往往不够精细，且常包含错误边缘。本研究采用级联集成Canny算子来解决这些问题并实现物体边缘检测。我们在Python环境中使用最具挑战性的Fresh and Rotten数据集与Berkeley数据集对提出方法进行验证。实验结果表明，无论是性能指标还是输出图像质量，所获结果均优于指定的边缘检测网络。