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An Image Edge Detection Algorithm Based on Multi-Feature Fusion

Computers, Materials & Continua 2022年第12期73卷 4995-5009页

作者： Zhenzhou Wang Kangyang Li Xiang Wang Antonio Lee School of Information Science and Engineering Hebei University of Science and TechnologyShijiazhuang050000China Department of Geophysical Engineering Karadeniz Technical UniversityTrabzon61080Turkey

Edge detection is one of the core steps of image processing and computer *** and fine image edge will make further target detection and semantic segmentation more ***-Nested edge detection(HED)edge detection network has been proved to be a deep-learning network with better performance for edge ***,it is found that when the HED network is used in overlapping complex multi-edge scenarios for automatic object *** will be detected edge incomplete,not smooth and other *** solve these problems,an image edge detection algorithm based on improved HED and feature fusion is *** the one hand,features are extracted using the improved HED network:the HED convolution layer is *** residual variable convolution block is used to replace the normal convolution enhancement model to extract features from edges of different sizes and ***,the empty convolution is used to replace the original pooling layer to expand the receptive field and retain more global information to obtain comprehensive feature *** the other hand,edges are extracted using Otsu algorithm:Otsu-Canny algorithm is used to adaptively adjust the threshold value in the global scene to achieve the edge detection under the optimal threshold ***,the edge extracted by improved HED network and Otsu-Canny algorithm is fused to obtain the final *** results show that on the Berkeley University Data Set(BSDS500)the optimal data set size(ODS)F-measure of the proposed algorithm is 0.793;the average precision(AP)of the algorithm is 0.849;detection speed can reach more than 25 frames per second(FPS),which confirms the effectiveness of the proposed method.

关键词： Computer vision edge detection deep learning feature fusion residual convolution

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