Underwater Sonar Image Detection: A Novel Quantum-Inspired Shuffled Frog Leaping Algorithm

WANG Xingmei; LIU Shu; LI Qiming; LIU Zhipeng

doi:10.1049/cje.2018.03.006

Volume 27 Issue 3

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Article Navigation > Chinese Journal of Electronics > 2018 > 27(3): 588-594

WANG Xingmei, LIU Shu, LI Qiming, et al., “Underwater Sonar Image Detection: A Novel Quantum-Inspired Shuffled Frog Leaping Algorithm,” Chinese Journal of Electronics, vol. 27, no. 3, pp. 588-594, 2018, doi: 10.1049/cje.2018.03.006

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WANG Xingmei, LIU Shu, LI Qiming, et al., “Underwater Sonar Image Detection: A Novel Quantum-Inspired Shuffled Frog Leaping Algorithm,” Chinese Journal of Electronics, vol. 27, no. 3, pp. 588-594, 2018, doi: 10.1049/cje.2018.03.006

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Underwater Sonar Image Detection: A Novel Quantum-Inspired Shuffled Frog Leaping Algorithm

doi: 10.1049/cje.2018.03.006

1.
College of Computer Science and Technology, Harbin Engineering University, Harbin 150001, China;
2.
College of National Secrecy, Harbin Engineering University, Harbin 150001, China;
3.
Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China

Funds: This work is supported by the National Natural Science Foundation of China (No.41306086).

More Information

Corresponding author: LIU Shu (corresponding author) was born in 1994. He received the B.E. degree from Harbin Engineering University. He is now a master candidate of Institute of Automation, Chinese Academy of Sciences. His research interests include artificial intelligence and sonar image processing. (Email:2672644147@qq.com)
Received Date: 2017-10-27
Rev Recd Date: 2018-01-12
Publish Date: 2018-05-10

Abstract

Abstract

Object detection plays an important role in the underwater object recognition technology of sonar equipment. We propose a Novel quantum-inspired shuffled frog leaping algorithm (NQSFLA) to obtain more accurate detection results in this paper. The proposed NQSFLA adopts a fitness function combining intra-class difference with inter-class difference to evaluate the frog position more accurately and a new quantum evolution update strategy to improve the searching ability in the searching process. In order to avoid the disadvantages of Quantuminspired shuffled frog leaping algorithm (QSFLA), a fuzzy membership matrix with spatial information model is developed, which can remove isolated regions and further improve the detection accuracy. Segmentation, distribution and noise entropy (SDNE) model is also proposed to quantitatively evaluate the detection results. The detection results of the original sonar images demonstrate the effectiveness and adaptability of the proposed method.
- Quantum-inspired shuffled frog leaping algorithm (QSFLA),
- Underwater sonar image,
- Fitness function,
- Update strategy,
- Fuzzy membership matrix

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References

Wang Xingmei, Guo Longxiang, Yin Jingwei, et al., "Narrowband Chan-Vese model of sonar imagesegmentation:A adaptive ladder initialization approach", Applied Acoustics, Vol.113, pp.238-254, 2016.

Eusuff Muzaffar M. and Lansey Kevin E., "Optimization of water distribution network design using the shuffled frog leaping algorithm", Water Resources Planning and Management, Vol.129, No.3, pp.210-225, 2003.

B.A. Norman and J.C. Bean, "A genetic algorithm methodology for complex scheduling problems", Naval Research Logistics, Vol.46, No.2, pp.199-211, 2015.

Ren Junliang, Xing Qinghua, Li Longyue, et al., "A model of distributed sensors scheduling and self-adaptive probability particle swarm optimization algorithm", Acta Electronica Sinica, Vol.43, No.9, pp.1756-1762, 2015. (in Chinese)

Luo Jian-Ping, Li Xia and Chen Min-Rong, "Improved shuffled frog leaping algorithm for solving CVRP", Electronics and Information Technology, Vol.33, No.2, pp.429-434, 2011.

Fan Tang-Huai, Lv Li and Zhao Jia, "Improved shuffled frog leaping algorithm and its application in node localization of wireless sensor network", Intelligent Automation and Soft Computing, Vol.18, No.7, pp.807-818, 2012.

Roy Priyanka, Roy Pritam and Chakrabarti Abhijit, "Modified shuffled frog leaping algorithm with genetic algorithm crossover for solving economic load dispatch problem with valve-point effect", Applied Soft Computing, Vol.13, No.11, pp.4244-4252, 2013.

Wang Lianguo and Gong Yaxing, "A Fast Shuffled Frog Leaping Algorithm", Proc. of International Conference on Natural Computation (ICNC2013), Shenyang, China, pp.369-373, 2013.

Taher Niknam, Bahman Bahmani Firouzi and Hasan Doagou Mojarrad, "A new evolutionary algorithm for non-linear economic dispatch", Expert Systems with Applications, Vol.40, No.1, pp.397-398, 2013.

Xingmei Wang, Shu Liu, Jianchuang Sun, et al., "A Novel Quantum Genetic Algorithm for Detection Sonar Image", Proc. of Chinese Control and Decision Conference, Yinchuan, China, pp.2020-2025, 2016.

Zhang Biao, Qi Hong, Sun Shuangcheng, et al., "Solving inverse problems of radiative heat transfer and phase change in semitransparent medium by using improved quantum particle swarm optimization", International Journal of Heat and Mass Transfer, Vol.85, No.1, pp.300-310, 2015.

Weiping Ding, Jiandong Wang, Zhijin Guan, et al., "Enhanced minimum attribute reduction based on quantum-inspired shuffled frog leaping algorithm", Journal of Systems Engineering and Electronics, Vol.24, No.3, pp.426-434, 2013.

Hongyuan Gao and Wen Cui, "A quantum-inspired shuffled frog leaping algorithm and its application in cognitive radio", International Journal of Digital Content Technology and Its Applications (JDCTA), Vol.6, No.20, pp.32-42, 2012.

Wang Lianguo and Gong Yaxing, "Quantum binary shuffled frog leaping algorithm", Proc. of International Conference on Instrumentation and Measurement, Computer, Communication and Control, Shenyang, China, pp.1655-1659, 2013.

Weiping Ding and Jiandong Wang, "A minimum attribute selfadaptive cooperative co-evolutionary reduction algorithm based on quantum elitist frogs", Journal of Computer Research and Development, Vol.54, No.4, pp.743-753, 2014.

WANG Shuliang, WANG Dakui, LI Caoyuan, et al., "Clustering by fast search and find of density peaks with data field", Chinese Journal of Electronics, Vol.25, No.3, pp.397-402, 2016.

Xiao Jing, Yan YuPing, Zhang Jun, et al., "A quantum-inspired genetic algorithm for k-means clustering", Expert Systems with Applications, Vol.37, No.7, pp.4966-4973, 2010.

Dagher Issam, "Complex fuzzy c-means algorithm", Artificial Intelligence Review, Vol.38, No.1, pp.25-39, 2012.

John A. Fawcett, Anna Crawford and David Hopkin, "Computer-aided detection of targets from the CITADEL trial Klein sonar data", Defence Research Reports Canada C Atlantic, pp.22-40, 2006.

DongPeng Wen, Tao Jin and ShiXian Qu, "Statistical analysis on DNA sequence of bacteria based on super information entropy", Journal of Xian University of Posts and Telecommunications, Vol.18, No.3, pp.23-24, 2013.

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