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作者:

Salem, Mostafa Hamdy (Salem, Mostafa Hamdy.) | Li, Yujian (Li, Yujian.) | Liu, Zhaoying (Liu, Zhaoying.) | AbdelTawab, Ahmed M. (AbdelTawab, Ahmed M..)

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Scopus SCIE

摘要:

Deep learning has been used to improve intelligent transportation systems (ITS) by classifying ship targets in interior waterways. Researchers have created numerous classification methods, but they have low accuracy and misclassify other ship targets. As a result, more research into ship classification is required to avoid inland waterway collisions. We present a new convolutional neural network classification method for inland waterways that can classify the five major ship types: cargo, military, carrier, cruise, and tanker. This method can also be used for other ship classes. The proposed method consists of four phases for the boosting of classification accuracy for Intelligent Transport Systems (ITS) based on convolutional neural networks (CNNs); efficient augmentation method, the hyper-parameter optimization (HPO) technique for optimum CNN model parameter selection, transfer learning, and ensemble learning are suggested. All experiments used Kaggle's public Game of Deep Learning Ship dataset. In addition, the proposed ship classification achieved 98.38% detection rates and 97.43% F1 scores. Our suggested classification technique was also evaluated on the MARVEL dataset. This dataset includes 10,000 image samples for each class and 26 types of ships for generalization. The suggested method also delivered an excellent performance compared to other algorithms, with performance metrics with an accuracy of 97.04%, a precision of 96.1%, a recall of 95.92%, a specificity of 96.55%, and a 96.31% F1 score.

关键词:

transfer learning particle swarm optimization ensemble learning convolutional neural network deep learning

作者机构:

  • [ 1 ] [Salem, Mostafa Hamdy]Beijing Univ Technol, Fac Informat Technol, Beijing 100124, Peoples R China
  • [ 2 ] [Liu, Zhaoying]Beijing Univ Technol, Fac Informat Technol, Beijing 100124, Peoples R China
  • [ 3 ] [Li, Yujian]Guilin Univ Elect Technol, Sch Artificial Intelligence, Guilin 541004, Peoples R China
  • [ 4 ] [AbdelTawab, Ahmed M.]Misr Univ Sci & Technol MUST, Fac Engn, Elect & Commun Dept, Giza 12566, Egypt

通讯作者信息:

  • [Salem, Mostafa Hamdy]Beijing Univ Technol, Fac Informat Technol, Beijing 100124, Peoples R China;;

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来源 :

APPLIED SCIENCES-BASEL

年份: 2023

期: 3

卷: 13

2 . 7 0 0

JCR@2022

ESI学科: ENGINEERING;

ESI高被引阀值:19

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