Computer Science

Computer Science ›› 2022, Vol. 49 ›› Issue (6A): 211-216.doi: 10.11896/jsjkx.220300216

• Intelligent Computing • Previous Articles     Next Articles

Fishing Type Identification of Marine Fishing Vessels Based on Support Vector Machine Optimized by Improved Sparrow Search Algorithm

SHAN Xiao-ying1, REN Ying-chun2   

  1. 1 Pinghu Normal College,Jiaxing University,Pinghu,Zhejiang 314200,China
    2 College of Data Science,Jiaxing University,Jiaxing,Zhejiang 314001,China
  • Online:2022-06-10 Published:2022-06-08
  • About author:SHAN Xiao-ying,born in 1984,Ph.D.Her main research interests include pattern recognition and image processing.
    REN Ying-chun,born in 1982,Ph.D.His main research interests include machine learning and computer vision.
  • Supported by:
    Zhejiang Province Education Department Science Research Item(Y202044497) and Zhejiang Provincial Natural Science Foundation of China(LQ20F020027).

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Abstract: The identification of fishing type has significance for monitoring the fishing activities of motor vessels and maintaining the marine ecological balance.To protect the marine environment and improve the supervision efficiency of fishing vessels,a fi-shing type identification algorithm based on support vector machine optimized by the improved sparrow search algorithm (ISSA-SVM) is proposed.First,the t-distribution mutation operator is introduced to optimize the population selection,which improves the global search ability and local development ability of the original SSA.Second,the position update formula of the spectators of SSA is modified to further improve the convergence speed of the algorithm.Finally,the fishing type identification model ISSA-SVM is constructed by using ISSA to optimize the parameters of SVM.The experimental results on 3 546 fishing vessels show that compared with SVM,PSO-SVM,GWO-SVM and SSA-SVM,the fishing type identification model of ISSA-SVM proposed in this paper has higher accuracy and faster convergence speed.

Key words: t distribution, Fishing type identification, Fitness value, Sparrow search algorithm, Support vector machine

CLC Number: 

  • TP391
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