Computer Science

Computer Science ›› 2023, Vol. 50 ›› Issue (6): 194-199.doi: 10.11896/jsjkx.220700145

• Computer Graphics & Multimedia • Previous Articles     Next Articles

PSwin:Edge Detection Algorithm Based on Swin Transformer

HU Mingyang1,2, GUO Yan1,2, JIN Yangshuang2   

  1. 1 Suzhou Institute for Advanced Research,University of Science and Technology of China,Suzhou,Jiangsu 215123,China
    2 School of Software Engineering,University of Science and Technology of China,Suzhou,Jiangsu 215123,China
  • Received:2022-07-15 Revised:2022-10-27 Online:2023-06-15 Published:2023-06-06
  • About author:HU Mingyang,born in 1997,master.His main research interests include computer vision and natural language processing.GUO Yan,born in 1981,lecturer.Her main research interests include information security,blockchain and NLP.

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Abstract: As a traditional computer vision algorithm,edge detection has been widely used in real-world scenarios such as license plate recognition and optical character recognition.When edge detection is used as the basis for higher-level algorithms,such as target detection,semantic segmentation and other algorithms.Edge detection can also be applied to urban security,autonomous driving and other fields.A good edge detection algorithm can effectively improve the efficiency and accuracy of the above compu-ter vision tasks.The difficulty of the edge extraction task lies in the size of the target and the difference of edge details,so the edge extraction algorithm needs to be able to effectively deal with edges of different scales.In this paper,the Transformer is applied to the edge extraction task for the first time,and a novel feature pyramid network is proposed to make full use of the multi-scale and multi-level features of the backbone network.PSwin uses a self-attention mechanism,which can extract global structural information in images more efficiently than convolutional neural network architectures.When evaluated on the BSDS500 dataset,the proposed PSwin edge detection algorithm achieves the best performance,with an ODS F-measure of 0.826 and an OIS of 0.841.

Key words: Edge detection, Feature pyramid network, Visual attention, Transfer learning, BSDS500

CLC Number: 

  • TP391
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