Object auto-recognition for underwater targets

Authors:
Tang Xu-Dong;Pang Yong-Jie;Li Ye;Zhang He
Affiliations:
Key lab of autonomous underwater vehicle, Harbin Engineering University, Harbin, China;Key lab of autonomous underwater vehicle, Harbin Engineering University, Harbin, China;Key lab of autonomous underwater vehicle, Harbin Engineering University, Harbin, China;Key lab of autonomous underwater vehicle, Harbin Engineering University, Harbin, China
Venue:
CCDC'09 Proceedings of the 21st annual international conference on Chinese control and decision conference
Year:
2009

Citing 3
Cited 0

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Abstract

The affine invariants is constructed based on region moments in order to eliminate the negative effects, which are brought by the underwater images under the influence of the lighting condition and some character of water media. Aiming at the draw backs of traditional BP neural network, such as converging slowly and tending to get into the local minimize, a new method of designing BP neural net works based on immune genetic algorithm (IGA) is proposed. The mechanisms of diversity maintaining and antibody density regulation exhibited in a biological immune system are introduced into IGA based on genetic algorithm (GA). The proposed algorithm overcome the problems of GA on search efficiency, individual diversity and premature, and enhanced the convergent performance effectively. The affine invariant features of four different objects are extracted and selected as the input of the trained neural network. The feasibility and advantages of this method are demonstrated by the experimental results.