Underwater Image Classification Based on EfficientnetB0 and Two-Hidden-Layer Random Vector Functional Link

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外文摘要：The ocean plays an important role in maintaining the equilibrium of Earth's ecology and providing humans access to a wealth of resources.To obtain a high-precision underwater image classification model,we propose a classification model that combines an EfficientnetB0 neural network and a two-hidden-layer random vector functional link network(EfficientnetB0-TRVFL).The features of underwater images were extracted using the EfficientnetB0 neural network pretrained via ImageNet,and a new fully connected layer was trained on the underwater image dataset using the transfer learning method.Transfer learning ensures the initial performance of the network and helps in the development of a high-precision classification model.Subsequently,a TRVFL was proposed to improve the classification property of the model.Net construction of the two hidden layers exhibited a high accuracy when the same hidden layer nodes were used.The parameters of the second hidden layer were obtained using a novel calculation method,which reduced the outcome error to improve the performance instability caused by the random generation of parameters of RVFL.Finally,the TRVFL classifier was used to classify features and obtain classification results.The proposed EfficientnetB0-TRVFL classification model achieved 87.28％,74.06％,and 99.59％accuracy on the MLC2008,MLC2009,and Fish-gres datasets,respectively.The best convo-lutional neural networks and existing methods were stacked up through box plots and Kolmogorov-Smirnov tests,respectively.The increases imply improved systematization properties in underwater image classification tasks.The image classification model offers important performance advantages and better stability compared with existing methods.

外文关键词：

underwater image classificationEfficientnetB0random vector functional linkconvolutional neural network

作者：

ZHOU Zhiyu、LIU Mingxuan、JI Haodong、WANG Yaming、ZHU Zefei

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作者单位：

School of Information Science and Technology,Zhejiang Sci-Tech University,Hangzhou 310018,China

Zhejiang Key Laboratory of DDIMCCP,Lishui University,Lishui 323000,China

School of Mechanical Engineering,Hangzhou Dianzi University,Hangzhou 310018,China

基金：

National Key R&D Program of ChinaKey R&D Program of Zhejiang ProvinceZhejiang Provincial Natural Science Foundation of China

项目编号：

2022YFC28039032021C03013LZ20F020003

出版年：

2024

DOI：

10.1007/s11802-024-5472-9

中国海洋大学学报(自然科学英文版)

中国海洋大学

中国海洋大学学报(自然科学英文版)

CSTPCD

影响因子：0.268

ISSN：1672-5182

年,卷(期)：2024.23(2)

参考文献量34