Sonar image library construction method based on modified CycleGAN model

A construction method and image library technology, applied in the field of sonar image library construction, can solve the problems of inability to collect sonar images, difficult to obtain data samples, and difficult operations, so as to alleviate mode collapse, increase stability, and speed up convergence. effect of speed

Pending Publication Date: 2020-12-29
NANJING UNIV OF AERONAUTICS & ASTRONAUTICS
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Problems solved by technology

Due to the complex underwater environment, the actual operation is difficult, and it is difficult to obtain a large number of data samples. However, many underwater engineering researches currently require large data samples. For example, the training of underwater target classification and detection networks using deep learning requires a large number of acoustic Sonar images, and a large number of sonar images cannot be collected in reality

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  • Sonar image library construction method based on modified CycleGAN model
  • Sonar image library construction method based on modified CycleGAN model
  • Sonar image library construction method based on modified CycleGAN model

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Experimental program
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Embodiment 1

[0042] Based on the improved CycleGAN implementation of the sonar image library method, through continuous training and optimization of the network, the mapping relationship between optics and sonar images is learned, and the corresponding sonar images can be directly synthesized from optical images by using the mapping relationship. The CycleGAN model training flow chart is as follows figure 2 As shown, the steps are as follows:

[0043] Step 1, prepare the optical image dataset and the sonar image dataset, and place them in the source domain (X domain) and the target domain (Y domain) respectively.

[0044] Step 2, set initialization parameters, including initial learning rate, batch size (ie, Batchsize value), choice of optimizer, and hyperparameters used to constrain the proportion of each loss function. The specific parameter settings are shown in Table 1.

[0045] Table 1

[0046] parameter size setting optimizer Adam initial learning rate 2e-4...

Embodiment 2

[0062] The present invention is to the improvement of CycleGAN model, and concrete improvement is divided into following several steps:

[0063] The CycleGAN network does not require a matching data set when implementing image style transfer, and has certain advantages in texture and color conversion. However, CycleGAN is prone to model collapse during the training process. Next, improve the CycleGAN loss function to Mitigating this from happening makes the final composited sonar image look better.

[0064] In step a, the log likelihood loss in the original GAN ​​loss formula is replaced by square loss, which can increase the stability of network training. The corresponding square loss function expression is as follows:

[0065]

[0066] Among them, X and Y represent the source domain (optical image domain) and target domain (sonar image domain); G and D represent the generator and discriminator respectively; G(x): X → Y, represents the generation from the optical image So...

Embodiment 3

[0080] According to CycleGAN to realize the idea of ​​image style transfer, its main task is to learn the mapping relationship between optical images and sonar images, and obtain the best possible mapping relationship through loss function and optimization algorithm. The specific experimental steps are as follows:

[0081] Step A, data set acquisition, that is, optical images and sonar images of different targets were collected respectively, and used to train the CycleGAN model. The present invention carries out synthesis experiments on the sonar images of three objects, which are respectively triangles, beverage bottles and tires. Therefore, corresponding optical and sonar images need to be acquired, and each experiment is performed independently. For optical images, since there is no ready-made modem available, they are obtained through web crawling (such as beverage bottles, tires) and manual drawing using drawing software (such as tripods). In this embodiment, the Gemini7...

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Abstract

The invention discloses a sonar image library construction method based on a modified CycleGAN model, and the method comprises the steps: improving a loss function of a network model on the basis of the CycleGAN model, achieving the construction of a sonar image library, i.e., constructing a sonar image through an optical image, and achieving the style migration from the optical image to the sonarimage. By improving the loss function of the CycleGAN network, the sonar image synthesis effect is improved, and a target detection network is designed to verify the effectiveness of the constructedsonar image data set.

Description

technical field [0001] The invention belongs to the field of sonar image processing, and in particular relates to a method for constructing a sonar image library. Background technique [0002] In recent years, with the vigorous development of the marine underwater acoustic detection industry, affected by the complexity of the water medium, electromagnetic waves are easily absorbed, resulting in a very short propagation distance in water, making it difficult to achieve long-distance detection tasks, while sound waves can travel tens of kilometers in water , is an ideal propagation signal. In the low-frequency band, the sound wave travels far away, and the noise recognition work (such as ship noise) can be realized directly by using the spectrum and other characteristics of the echo signal; while in the high-frequency band, sonar target imaging can be realized within a range of several hundred meters. At present, imaging sonar has gradually become one of the necessary equipme...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F16/51G06F16/55G06N3/04G06N3/08
CPCG06F16/51G06F16/55G06N3/08G06N3/045
Inventor 谢奎凡志邈刘雪夏伟杰卞俊寿怀韬姚可为
Owner NANJING UNIV OF AERONAUTICS & ASTRONAUTICS
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