Side Scan Sonar Shipwreck Image Recognition Algorithm based on Transfer Learning and Deep Learning

Abstract

Current underwater shipwreck side scan sonar samples are few and difficult to label. With small sample sizes, their image recognition accuracy with a convolutional neural network model is low. In this study, we proposed an image recognition method for shipwreck side scan sonar that combines transfer learning with deep learning. In the non-transfer learning, shipwreck sonar sample data were used to train the network, and the results were saved as the control group. The weakly correlated data were applied to train the network, then the network parameters were transferred to the new network, and then the shipwreck sonar data was used for training. These steps were repeated using strongly correlated data. Experiments were carried out on Lenet-5, AlexNet, GoogLeNet, ResNet and VGG networks. Without transfer learning, the highest accuracy was obtained on the ResNet network (86.27%). Using weakly correlated data for transfer training, the highest accuracy was on the VGG network (92.16%). Using strongly correlated data for transfer training, the highest accuracy was also on the VGG network (98.04%). In all network architectures, transfer learning improved the correct recognition rate of convolutional neural network models. Experiments show that transfer learning combined with deep learning improves the accuracy and generalization of the convolutional neural network in the case of small sample sizes.