用级联集成Canny算子提升复杂场景边缘检测精度
Defective Edge Detection Using Cascaded Ensemble Canny Operator
- 设计级联集成Canny算子，融合多模型优势
- 在Fresh、Rotten和Berkeley数据集上优于现有方法
- 适合需要高精度边缘提取的图像处理应用
边缘检测是计算机视觉中最具挑战性的问题之一，尤其在包含多种类型和尺寸物体的真实图像中识别边界尤为困难。基于集成学习的方法（结合多种骨干网络与注意力模块）已超越传统方法（如Sobel和Canny）。然而，在复杂场景下仍存在局限，且现有方法检测出的边缘常含误检。本文提出级联集成Canny算子，有效解决上述问题。在Fresh、Rotten和Berkeley三个高难度数据集上进行测试，结果表明该方法在性能指标与输出图像质量上均优于现有边缘检测网络。
原文摘要 · Abstract (English)
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
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。