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中最具挑战性的难题之一，这源于从包含各类尺寸物体的真实世界图像中识别边界和边缘的困难性。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，面对复杂场景图像时，这些算法仍面临挑战。此外，现有方法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，该方法在性能指标与输出图像质量方面均优于现有边缘检测网络。