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

作　　者：ZHOU Zhiyu LIU Mingxuan JI Haodong WANG Yaming ZHU Zefei 

机构地区：[1]School of Information Science and Technology,Zhejiang Sci-Tech University,Hangzhou 310018,China [2]Zhejiang Key Laboratory of DDIMCCP,Lishui University,Lishui 323000,China [3]School of Mechanical Engineering,Hangzhou Dianzi University,Hangzhou 310018,China

出　　处：《Journal of Ocean University of China》2024年第2期392-404,共13页中国海洋大学学报（英文版）

基　　金：support of the National Key R&D Program of China(No.2022YFC2803903);the Key R&D Program of Zhejiang Province(No.2021C03013);the Zhejiang Provincial Natural Science Foundation of China(No.LZ20F020003).

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 convolutional 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.

关 键 词：underwater image classification EfficientnetB0 random vector functional link convolutional neural network 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术] P71[自动化与计算机技术—计算机科学与技术]