基于深度学习的图像边缘检测算法综述

边缘检测是将图像中的突变的重要信息提取出来的过程，是计算机视觉领域研究热点，也是图像分割、目标检测与识别等多种中高层视觉任务的基础。近几年来，针对边缘轮廓线过粗以及检测精度不高等问题，业内提出了谱聚类、多尺度融合、跨层融合等基于深度学习的边缘检测算法。为了使更多研究者了解边缘检测的研究现状，首先，介绍了传统边缘检测的实现理论及方法；然后，总结了近年来基于深度学习的主要边缘检测方法，并依据实现技术对这些方法进行了分类，对其涉及的关键技术进行分析，发现对多尺度多层次融合与损失函数的选择是重要的研究方向。通过评价指标对各类方法进行了比较，可知边缘检测算法在伯克利大学数据集（BSDS500）上的最优数据集规模（ODS）经过多年研究从0.598提高到了0.828，接近人类视觉水平。最后，展示了边缘检测算法研究的发展方向。

Review of image edge detection algorithms based on deep learning

Edge detection is the process of extracting the important information of mutations in the image. It is a research hotspot in the field of computer vision and the basis of many middle-and high-level vision tasks such as image segmentation, target detection and recognition. In recent years, in view of the problems of thick edge contour lines and low detection accuracy, edge detection algorithms based on deep learning such as spectral clustering, multi-scale fusion, and cross-layer fusion were proposed by the industry. In order to make more researchers understand the research status of edge detection, firstly, the implementation theory and methods of traditional edge detection were introduced. Then, the main edge detection methods based on deep learning in resent years were summarized, and these methods were classified according to the implementation technologies of the methods. And the analysis of the key technologies of these methods show that the multi-scale multi-level fusion and selection of loss function was the important research directions. Various methods were compared to each other through evaluation indicators. It can be seen that the Optimal Dataset Scale (ODS) of edge detection algorithm on the Berkeley Segmentation Data Set and benchmark 500 (BSDS500) was increased from 0.598 to 0.828, which was close to the level of human vision. Finally, the development direction of edge detection algorithm research was forecasted.