Abstract
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%.
Highlights
Sonar imaging technology, including real aperture sidescan imaging, real aperture multi-beam imaging, synthetic aperture imaging, reversed synthetic aperture imaging etc., can achieve high resolution twodimensional sonar images in the range on hundreds meters
In order to compare the difference of training speed and final recognition accuracy between the traditional training method that start from the initial state and the training method based on transfer learning, a comparative experiment is designed based on AlexNet pre-trained network
This paper introduces an attempt to apply the new revolutionary technology Convolutional Neural Network (CNN) and transfer learning in the field of sonar image recognition
Summary
Sonar imaging technology, including real aperture sidescan imaging, real aperture multi-beam imaging, synthetic aperture imaging, reversed synthetic aperture imaging etc., can achieve high resolution twodimensional sonar images in the range on hundreds meters. By taking the pre-trained network in computer vision as the basis, the architecture and most of the parameters in the network is inherited from the original network, only the last few layers should be modified to realize specific domain recognition This method, knowing as fine-tuning, cannot only reduce the computational complexity in network training procedure, and reduces the size of the data required for network training. Their activations can be computed with a matrix multiplication followed by a bias offset
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