A seabed sediment classification model based on PSO-AlexNet

Extracting feature parameters is the most important part of the current acoustic sediment classification methods. Aiming at the problems of multiple selectivity and instability in the sediment classification after feature parameters extracted from the sonar image, it was proposed that inputting sonar images directly into the improved convolutional neural network(CNN) for seabed sediment classification without extracting image features. This paper compared the classification results of 12 types of sediment classification models based on feature extraction and four classical CNN models. It showed that the classification accuracy of the former ranges from 39% to 92.6%, which was greatly influenced by the types of feature parameters and classifiers; the classification accuracy of the latter ranged from 84.2% to 89.6%, among which AlexNet presents the best effects in CNN. Then the particle swarm optimization(PSO) algorithm was used to optimize the five training parameters in the AlexNet model. The same training set and test set were used for comparative experiments. And the results showed that the PSO-AlexNet model can obtain 93.8% of the seabed sediment classification accuracy. And the classification effects are better than the sediment classification model based on feature extraction.