%0 Journal Article %A LI Cuijin %A QU Zhong %T Review of image edge detection algorithms based on deep learning %D 2020 %R 10.11772/j.issn.1001-9081.2020030314 %J Journal of Computer Applications %P 3280-3288 %V 40 %N 11 %X 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. %U http://www.joca.cn/EN/10.11772/j.issn.1001-9081.2020030314