Sonar image recognition based on fine-tuned convolutional neural network

To solve the problem of sonar image recognition, a sonar image recognition method based on fine-tuned Convolutional Neural Network (CNN) is proposed in this paper. With the development of deep learning, CNN shows impressive performance in image recognition. However, massive data is needed to train a CNN from beginning. Through fine-tuning pre-trained CNN can help us training CNN from relatively high starting points, based on those pre-trained CNNs, only few data is needed to retrain a CNN which focus on sonar image recognition. A scaled model experiment shows that based on the architecture of AlexNet, compared with the traditional learning method, the transfer learning method can achieve higher recognition accurate rate of 95.81% and less training time. Moreover, this paper also compared 6 pre-trained networks, among those networks, VGG16 can achieve the highest recognition rate of 99.48%