Ship sample expansion method in infrared image based on style generative adversarial network

An infrared image and sample expansion technology, applied in the field of image processing, can solve the problems of limited number of expansion, poor realism of infrared samples, lack of diversity of expanded infrared samples, etc.

Active Publication Date: 2020-10-23
XIDIAN UNIV
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Problems solved by technology

[0005] The purpose of the present invention is to address the deficiencies of the above-mentioned prior art, and propose a method for expanding ship samples in infrared images based on pattern generation confrontation network, aiming at solving the problem of complex simulation proces...

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  • Ship sample expansion method in infrared image based on style generative adversarial network
  • Ship sample expansion method in infrared image based on style generative adversarial network
  • Ship sample expansion method in infrared image based on style generative adversarial network

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Embodiment Construction

[0051] The present invention will be described in further detail below in conjunction with the accompanying drawings.

[0052] Refer to attached figure 1 , the implementation steps of the present invention are further described in detail.

[0053] Step 1, get the training set.

[0054] Select at least 2000 real-time infrared images, each of which contains a ship target; scale and crop each image to 256×256 to form a training set.

[0055] Step 2, build the generator network.

[0056] Build a generator network and set the parameters of each layer of the network as the generator network of the style generative adversarial network.

[0057] Refer to attached figure 2 , to further describe in detail the generator network structure in the pattern generation adversarial network constructed by the present invention.

[0058] The structure of the generator network is as follows: constant matrix layer → 1st noise modulation layer → 1st adaptive pattern modulation layer → 1st deco...

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Abstract

The invention provides a ship sample expansion method in an infrared image based on a style generative adversarial network. The method mainly solves the problems that in the prior art, the authenticity of generated infrared images is poor due to complex simulation modeling, the collection difficulty of visible light-infrared image photoelectric conversion training samples is large, and the numberof training sets is small, so that the expanded infrared images are lack of diversity. The method comprises the following steps: (1) selecting real-shot infrared images to form the training sets; (2)constructing a generator network; (3) constructing a discriminator network; (4) constructing a style generative adversarial network; (5) training a discriminator network; (6) training a generator network; (7) training a style generative adversarial network; and (8) outputting an infrared image sample by using the trained generator network to complete the expansion of the infrared image sample. According to the method, a large number of infrared ship samples can be generated, and the sense of reality and diversity of expanded samples are effectively improved.

Description

technical field [0001] The invention belongs to the technical field of image processing, and further relates to a method for expanding ship samples in infrared images based on styleGAN (style-based Generative Adversarial Network) in the field of deep learning. The invention can expand the ship samples in the infrared image so as to provide abundant data sets for the training of the infrared target detection and recognition algorithm. Background technique [0002] Infrared imaging technology is often used in target detection, identification and tracking due to its strong target detection ability and strong anti-interference ability. Due to the complex infrared characteristics of the target, which change significantly with temperature conditions, it is difficult to detect and recognize infrared targets. In order to improve the ability to detect and recognize infrared targets, a large number of infrared images are generally required to train the detection and recognition algori...

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Application Information

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IPC IPC(8): G06K9/62G06N3/04G06N3/08
CPCG06N3/08G06N3/045G06F18/214
Inventor 吴鑫汪钰邹俊锋李俊儒黄曦
Owner XIDIAN UNIV
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