Computer Science

Computer Science ›› 2020, Vol. 47 ›› Issue (6): 144-150.doi: 10.11896/jsjkx.190700121

• Computer Graphics & Multimedia • Previous Articles     Next Articles

Object-level Edge Detection Algorithm Based on Multi-scale Residual Network

ZHU Wei1,2, WANG Tu-qiang1, CHEN Yue-feng1, HE De-feng1,2   

  1. 1 College of Information Engineering,Zhejiang University of Technology,Hangzhou 310023,China
    2 United Key Laboratory of Embedded System of Zhejiang Province,Hangzhou 310023,China
  • Received:2019-07-17 Online:2020-06-15 Published:2020-06-10
  • About author:ZHU Wei,born in 1982,Ph.D,associate professor.His main research interests include pattern recognition and intelligent system.
  • Supported by:
    This work was supported by the Natural Science Foundation of Zhejiang Province (LY17F010013) and National Natural Science Foundation of China (61401398)

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Abstract: Object-level edge detection technology is a key basic technology in the field of intelligent vision processing.However,there are some problems in the edge detection results based on convolutional neural network,such as low resolution and high noise.Therefore,an object-level edge detection algorithm based on multi-scale residual network is proposed.Firstly,a hybrid dilated convolution residual block is designed to replace the ordinary convolution kernel in the original residual network to enlarge the receptive field of the network.Secondly,a multi-scale feature enhancement module is designed to extract multi-scale features from edge information to enlarge the information receiving domain of the network.Finally,a pyramid multi-scale feature fusion module combining top-level semantic features is designed to fuse the feature information at different scales and output the image after edge detection.In order to verify the effectiveness of the proposed algorithm,experimental analysis is performed on the public dataset BSDS500.The experimental results show that compared with existing algorithms,the proposed algorithm has better edge detection effect,and the objective indicators ODS,OIS and AP are increased to 0.819,0.838 and 0.849,respectively,meanwhile the subjective detection effect is closer to the real value with less noise.

Key words: Dilated convolution, Multi-scale feature enhancement, Pyramid feature fusion structure, Residual network

CLC Number: 

  • TP391
[1]CANNY J F.A Computation Approach to Edge Detection [M].A Computational Approach to Edge Detection,1986.
[2]KONISHI S,YUILLE A L,COUGHLAN J M,et al.Statistical Edge Detection:Learning and Evaluating Edge Cues[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2003,25(1):57-74.
[3]ARBELÁEZ P,MAIRE M,FOWLKES C,et al.Contour Detection and Hierarchical Image Segmentation[J].IEEE Transactions on Pattern Analysis & Machine Intelligence,2011,33(5):898-916.
[4]DOLLÁR,PIOTR,ZITNICK C L.Fast Edge Detection Using Structured Forests[J].IEEE Transactions on Pattern Analysis &Machine Intelligence,2014,37(8):1558-1570.
[5]KRIZHEVSKY A,SUTSKEVER I,HINTON G E.ImageNet Classification with Deep Convolutional Neural Networks[C]//Advances in Neural Information Processing Systems.2012:1097-1105.
[6]GANIN Y,LEMPITSKY V.N4-Fields:Neural Network Nearest Neighbor Fields for Image Transforms[C]//Asian Confe-rence on Computer Vision.Springer,Cham,2014:536-551.
[7]BERTASIUS G,SHI J,TORRESANI L.DeepEdge:A Multi-scale Bifurcated Deep Network for Top-down Contour Detection[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.2015:4380-4389.
[8]XIE S,TU Z.Holistically-Nested Edge Detection[C]//Procee-dings of the IEEE International Conference on Computer Vision.2015:1395-1403.
[9]LIU Y,CHENG M M,HU X,et al.Richer Convolutional Features for Edge Detection[C]//Proceedings of the IEEE Confe-rence on Computer Vision and Pattern Recognition.2017:3000-3009.
[10]YU F,KOLTUN V.Multi-scale Context Aggregation by Dilated Convolutions[J].arXiv:1511.07122.
[11]HE K,ZHANG X,REN S,et al.Deep Residual Learning for Ima-ge Recognition[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.2016:770-778.
[12]WANG P,CHEN P,YUAN Y,et al.Understanding Convolution for Semantic Segmentation[C]//IEEE Winter Conference on Applications of Computer Vision.2018:1451-1460.
[13]LIN T Y,DOLLÁR P,GIRSHICK R,et al.Feature Pyramid Networks for Object Detection[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.2017:2117-2125.
[14]MOTTAGHI R,CHEN X,LIU X,et al.The Role of Context for Object Detection and Semantic Segmentation in the Wild[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.2014:891-898.
[15]KOKKINOS I.Pushing the Boundaries of Boundary Detection using Deep Learning[J].arXiv:1511.07386.
[16]YANG J,PRICE B,COHEN S,et al.Object Contour Detection with a Fully Convolutional Encoder-Decoder Network[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.IEEE,2016:193-202.
[17]LIN T Y,GOYAL P,GIRSHICK R,et al.Focal Loss for Dense Object Detection[J].IEEE Transactions on Pattern Analysis & Machine Intelligence,2017,42(2):318-327.
[18]ZITNICK C L,DOLLÁR P.Edge Boxes:Locating Object Proposals from Edges[C]//European Conference on Computer Vision.Springer,Cham,2014:391-405.
[19]SIMONYAN K,ZISSERMAN A.Very Deep Convolutional Networks for Large-Scale Image Recognition[J].arXiv:1409.1556.
[20]SZEGEDY C,LIU W,JIA Y,et al.Going deeper with convolutions[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.IEEE,2015:1-9.
[21]HUANG G,LIU Z,VAN DER MAATEN L,et al.Densely connected convolutional networks[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.IEEE 2017:4700-4708.
[22]KIVINEN J,WILLIAMS C,HEESS N.Visual Boundary Prediction:A Deep Neural Prediction Network and Quality Dissection[C]//International Conference on Artificial Intelligence and Statistics.PMLR,2014:512-521.
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