文章摘要
巩文静,田杰,李宝奇,刘纪元.基于改进MobilenetV2网络的声光图像融合水下目标分类方法*[J].,2022,41(3):462-470
基于改进MobilenetV2网络的声光图像融合水下目标分类方法*
Acoustic-optical Image Fusion Underwater Target Classification Method Based on Improved MobilenetV2
投稿时间:2021-04-28  修订日期:2022-04-11
中文摘要:
      针对小样本条件下水下目标分类准确率低、计算资源量大的问题,提出一种声光图像融合目标分类方法。首先,对MobilenetV2网络进行改进,减小网络的复杂度,改善网络拟合效果;其次,设计一种融合网络结构,将两种图像并行输入网络进行特征提取,在中间层按通道实现特征图融合。在真实数据集上对网络进行训练,结果表明,改进的MobilenetV2网络对水下目标的分类准确率相比改进前提升1.1%,参数量和计算量分别减少44%和23%;使用不同算法在不同网络层位置进行融合,分类准确率相比融合前平均提升4.9%,4.4%,5.2%,5.9%,5.8%,6.0%,网络的性能较为稳定。
英文摘要:
      To solve the problems of low accuracy and high consumption of underwater target classification under the condition of small samples, an acoustic-optical image fusion classification method is proposed. Firstly, the mobilenetv2 network is improved to reduce the network complexity and improve the network fitting effect. Secondly, a fusion network structure is designed, which inputs acoustic image and optical image in parallel for feature extraction. Then, the extracted feature maps are combined in the middle layer and the real data are used to train the network. Results show that the average accuracy of the improved mobilenetv2 network is 1.1% higher than before in the classification experiments, the parameters and calculation are reduced by 44% and 23% respectively. Using different algorithms at different locations, the classification accuracy respectively improved 4.9%, 4.4%, 5.2%, 5.9%, 5.8% and 6.0% compared with pre fusion network, indicating that the performance of the fusion network in classification accuracy and stability are improved further.
DOI:10.11684/j.issn.1000-310X.2022.03.017
中文关键词: 改进MobilenetV2  声学图像  光学图像  图像融合  水下目标分类
英文关键词: underwater acoustic image  OTSU algorithm  shape feature  invariant moment  target recognition
基金项目:中国科学院国防科技重点实验室基金项目(CXJJ-20S035)
作者单位E-mail
巩文静 中国科学院声学研究所 gongwenjing@mail.ioa.ac.cn 
田杰 中国科学院声学研究所 tianjie@mail.ioa.ac.cn 
李宝奇 中国科学院声学研究所 libaoqi@mail.ioa.ac.cn 
刘纪元 中国科学院声学研究所 ljy@mail.ioa.ac.cn 
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