Research of Diver Sonar Image Recognition Based on Support Vector Machine

Ke Li; Chong Lun Li; Wei Zhang

doi:10.4028/www.scientific.net/AMR.785-786.1437

Paper Titles

Determination of Parameters of Leakage of Propane from the Tank
p.1413

τ-D Decomposition to the One Dimensional Delay Differential Equation by Fixed the Coefficient b<0
p.1418

A Variable Weight Function Model in Geological Variables Function Research
p.1423

Identification of Nonlinear Systems Using Parallel Laguerre-NN Model
p.1430

Research of Diver Sonar Image Recognition Based on Support Vector Machine
p.1437

Sensitivity Analysis in Artificial Neural Network and it's Applications on the Research of Attributes Correlation
p.1441

Using GM(1,1) to Clean-Table Offensive Techniques
p.1447

Existence and Blow-Up of Solutions for a Degenerate Semilinear Parabolic Equation
p.1454

Perspective on Effect of the New Model "Contest of Comprehensive Chemistry Experiment"
p.1461

HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 785-786Research of Diver Sonar Image Recognition Based on...

Research of Diver Sonar Image Recognition Based on Support Vector Machine

Abstract:

To recognize small diver target from the dim special diver sonar images accurately, the Support Vector Machine method is used as classifier. According to the main characteristics of diver, five feature parameters, including Average-scale, Velocity, Shape, Direction, Included angle, are chosen as the input of characteristics vectors to train the net. And then the testing images are classified and identified. The experimental results show that accuracy rate of recognition reaches 94.5% for as many as 200 testing images. The experiment indicates that small object recognition from complex sonar images based on the right selection of feature parameters is of good performance by using the SVM method as well as good engineering foreground.

You might also be interested in these eBooks

Current Trends in the Development of Industry

View Preview

Info:

Periodical:

Advanced Materials Research (Volumes 785-786)

Pages:

1437-1440

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.785-786.1437

Citation:

Cite this paper

Online since:

September 2013

Authors:

Ke Li, Chong Lun Li, Wei Zhang

Keywords:

Diver, Feature, Recognition, Support Vector Machine (SVM)

Export:

RIS, BibTeX

Price:

Permissions CCC:

Request Permissions

Permissions PLS:

Request Permissions

Сopyright:

Citation:

References

[1] Mao Dun, Liu Zhong, Cheng Yuanguo, Underwater small target detection algorithm based on diver detection sonar image sequences. Journal of Transduction Technology, Vol. 24, pp.27-32, July 2011. (in Chinese).

Google Scholar

[2] Si Majianglong and Deng Changshou, Face recognition system based on BP neural network. Journal of Jiujiang University (natural sciences), Vol. 94, pp.23-26, March 2011. (in Chinese).

Google Scholar

[3] He Shizhao, Yang Xuanfang, Chen Xiaojuan, Comparisons between a support vector machine and BP neural network for video image fire detection. CAAI Transactions on Intelligent Systems, Vol. 6, pp.339-343, August 2011. (in Chinese).

Google Scholar

[4] Feng Chao, He Junji, Shi Li, Recognition of car model based on support vector machine. Journal of Shanghai Maritime University, Vol. 32, pp.85-89, September 2. 11. (in Chinese).

Google Scholar

[5] Cui Bingde, Remote sensing image classification based on SVM classifier. Computer Engineering and Application, Vol. 47, pp.189-191, 2011．(in Chinese).

Google Scholar

[6] Crawford A. M and Vance Crowe D. Observations from Demonstrations of Several Commercial Diver Detection Sonar Systems [J]. Oceans, pp.1-3, (2007).

DOI: 10.1109/oceans.2007.4449325

Google Scholar