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

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.