Researching | Research on Road Extraction Algorithm Based on Residual Neural Networks

Optoelectronic Technology, Volume. 40, Issue 1, 6(2020)

Research on Road Extraction Algorithm Based on Residual Neural Networks

Wei XIONG^1,2,3, Laifu GUAN¹, Lei TONG¹, Chuansheng WANG¹, Min LIU^1,2, and Chunyan ZENG^1,2

Author Affiliations

¹School of Electrical and Electronic Engineering, Hubei University of Technology, Wuhan 430068, CHN

²Hubei Collaborative Innovation Center for High⁃Efficiency Utilization of Solar Energy, Hubei University of Technology, Wuhan, 430068, CHN

³Department of Computer Science and Engineering, University of South Carolina, Columbia, SC 29201, USA

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Figures&Tables (7)

References (24)

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Figures & Tables(7)

Fig. 1. Algorithm overall framework

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Fig. 2. Residual block

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Fig. 3. Dilated convolution

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Fig. 4. Dilated convolution module

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Fig. 5. Decoder module

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Fig. 6. The visualized results

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Table 1. Performance comparison of different algorithms

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Table 1. Performance comparison of different algorithms

	精确率/(%)	召回率/(%)	F1值/(%)	准确率/(%)
CRF^[5]	40.5	32.2	35.9	82.5
Minimum cost path^[6]	47.1	67.9	55.6	89.9
FCN^[17]	43.5	68.6	53.2	90.4
RSRCNN^[12]	60.6	72.9	66.2	92.4
Seg Net^[19]	77.3	76.5	76.8	95.7
Two⁃Step⁃DCNN^[15]	87.9	89.3	88.6	98.1
Road⁃RCF^[16]	85.8	98.5	91.5	96.3
U⁃Net(5 layer)	98.8	98.8	98.8	97.8
U⁃Net(7 layer)	98.8	98.7	98.8	97.7
Ours	98.9	98.8	98.9	97.9

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Wei XIONG, Laifu GUAN, Lei TONG, Chuansheng WANG, Min LIU, Chunyan ZENG. Research on Road Extraction Algorithm Based on Residual Neural Networks[J]. Optoelectronic Technology, 2020, 40(1): 6

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Paper Information

Category: Research and Trial-manufacture

Received: Jul. 29, 2019

Accepted: --

Published Online: Apr. 26, 2020

The Author Email:

DOI:10.19453/j.cnki.1005-488x.2020.01.002

Topics

laser devices and laser physics

Lasers and Laser Optics

laser manufacturing

Instrumentation, Measurement and Metrology