一种利用迁移学习训练卷积神经网络的声呐图像识别方法

朱兆彤; 付学志; 胡友峰

doi:10.11993/j.issn.2096-3920.2020.01.013

一种利用迁移学习训练卷积神经网络的声呐图像识别方法

doi: 10.11993/j.issn.2096-3920.2020.01.013

中国船舶重工集团公司第705研究所, 陕西西安, 710077, 2. 中国人民解放军92228部队, 北京, 102249

基金项目: 中国船舶重工集团公司第七〇五研究所博士后基金资助项目; 陕西省博士后科研项目资助(2017BSHQYXMZZ04)

详细信息

作者简介:
朱兆彤(1988-), 男, 博士, 工程师, 主要研究方向为声呐成像与目标识别.

中图分类号: TB566; TN911.73
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- 文章访问数: 711
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- 被引次数: 0
出版历程
- 收稿日期: 2019-01-16
- 修回日期: 2019-05-26
- 刊出日期: 2020-02-29

A Sonar Image Recognition Method Based on Convolutional Neural Network Trained through Transfer Learning

The 705 Research Institute, China Shipbuilding Industry Corporation, Xi’an 710077, China, 2. 92228th Unit, the People’s Liberation Army of China, Beijing, 102249

摘要

摘要: 针对利用传统训练方法进行声呐图像识别时缺乏数据的问题, 文中提出一种利用迁移学习训练卷积神经网络(CNN)实现声呐图像识别的方法。基于迁移学习的原理, 通过对已有的预训练网络进行微调与重新训练, 以减小对训练数据量的需求。随后利用缩比模型水池试验验证了该方法的有效性。试验结果表明, 基于AlexNet预训练网络, 相比传统的学习方法, 迁移学习方法可以利用较少的训练数据, 在较短的时间内通过训练达到95.81%的识别率。试验还对比了基于6种预训练网络进行迁移学习后的网络性能, 结果表明基于VGG16的迁移网络识别率最高, 可达到99.48%。最后, 试验结果验证了CNN具有一定的噪声容忍能力, 在较强噪声背景下, 能保证较高的识别率。
- 声呐成像 /
- 目标自动识别 /
- 卷积神经网络 /
- 迁移学习
Abstract: A sonar image recognition method using convolutional neural network trained by transfer learning is proposed aiming at the data shortage problem in making use of conventional training method for sonar image recognition. Based on the principle of transfer learning, the existing pre-trained network is fine-tuned and retrained to reduce the demand for training data. Pool experiment of the scale model verifies the effectiveness of this method. Experimental results show that, on the basis of AlexNet pre-trained network, the transfer learning method uses less training data compared with the conventional learning method, and achieves a recognition rate of 95.81% in a short time. The experiment also compares the performance of the network after the transfer learning based on six kinds of pre-trained networks, and the result shows that the recognition rate of the transfer network based on VGG16 is the highest, which can reach 99.48%. It is verified that the deeply convolutional neural network has certain noise tolerance ability, and can ensure a high recognition rate in the background of strong noise.
- sonar imaging /
- automatic target recognition /
- convolutional neural network /
- transfer learning

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参考文献(11)

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