Computer Engineering and Applications ›› 2023, Vol. 59 ›› Issue (5): 40-54.DOI: 10.3778/j.issn.1002-8331.2209-0122

• Research Hotspots and Reviews • Previous Articles     Next Articles

Overview of Image Edge Detection

XIAO Yang, ZHOU Jun   

  1. School of Electronics & Information Engineering, Liaoning University of Technology, Jinzhou, Liaoning 121000, China
  • Online:2023-03-01 Published:2023-03-01



  1. 辽宁工业大学 电子与信息工程学院,辽宁 锦州 121000

Abstract: The task of edge detection is to identify pixels with significant brightness changes as target edges, which is a low-level problem in computer vision, and edge detection has important applications in object recognition and detection, object proposal generation, and image segmentation. Nowadays, edge detection has produced several types of methods, such as traditional gradient-based detection methods and deep learning-based edge detection algorithms and detection methods combined with emerging technologies. A finer classification of these methods provides researchers with a clearer understanding of the trends in edge detection. Firstly, the theoretical basis and implementation methods of traditional edge detection are introduced; then the main edge detection methods in recent years are summarized and classified according to the methods used, and the core techniques used in them are introduced, such as branching structure, feature fusion and loss function. The evaluation indicators used to assess the algorithm’s performance are single-image optimal threshold(ODS) and frame per second(FPS), which are contrasted using the fundamental data set(BSDS500). Finally, the current state of edge detection research is examined and summarized, and the possible future research directions of edge detection are prospected.

Key words: edge detection, gradient operator, deep learning, feature fusion, loss function

摘要: 边缘检测的任务是将亮度变化明显的像素点识别为目标边缘,是计算机视觉低层级问题,并且边缘检测在对象识别和检测、对象提议生成、图像分割有着重要应用。如今,边缘检测已经产生了多类方法,如基于梯度的传统检测方法、基于深度学习的边缘检测算法,还有结合新兴技术的检测方法等。对这些方法进行更精细的分类,让研究者更清楚地了解边缘检测的发展趋势。对传统边缘检测的理论依据及实现方法做出介绍;详细介绍近年来主要的深度学习边缘检测方法,根据使用的方法进行分类,并对其中所使用的创新技术进行说明,如分支结构、特征融合和损失函数。衡量算法性能采用评估指标:单图最佳阈值(ODS)和帧数(FPS),在基础数据集(BSDS500)上进行对比。对边缘检测的研究现状进行分析和总结,对未来可能的研究方向进行展望。

关键词: 边缘检测, 梯度算子, 深度学习, 特征融合, 损失函数