A Comprehensive Review of Edge Detection Techniques

Edge detection is a basic task of computer vision. The purpose of edge detection is to extract the boundary information of the object in the image, which provides key support for downstream tasks. Traditional edge detection methods rely on hand-designed features and perform well in simple scenes, but when it comes to complex scenes, their performance is poor. With the rise of deep learning, methods based on convolutional neural network have greatly improved the accuracy and robustness of edge detection. In this paper, we systematically sort out the main methods in the field of edge detection, including traditional gradient operators, CNN-based deep learning methods, and emerging technologies. In addition, this paper also introduces the commonly used edge detection data set and Evaluation Index, which provides a comprehensive technical reference for researchers.