| Issue |
MATEC Web Conf.
Volume 283, 2019
The 2nd Franco-Chinese Acoustic Conference (FCAC 2018)
|
|
|---|---|---|
| Article Number | 04012 | |
| Number of page(s) | 5 | |
| Section | Acoustical Imaging | |
| DOI | https://doi.org/10.1051/matecconf/201928304012 | |
| Published online | 28 June 2019 | |
Sonar image recognition based on fine-tuned convolutional neural network
Xi’an Precision Machinery Research Institute, CSIC, 710075, Xi’an, China
* Corresponding author: Johntonzhu@163.com
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%.
© The Authors, published by EDP Sciences, 2019
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