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Infrared sample target detection method based on cyclic consistent adversarial network

A target detection and network technology, which is applied in the field of image processing, can solve the problems of limited influence, target detection network learning feature representation is not strong enough, and the quality of target detection information has not been improved.

Active Publication Date: 2020-11-10
TIANJIN UNIV
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AI Technical Summary

Problems solved by technology

However, there are limitations in data expansion. It only expands the scale of the data set and does not improve the quality of target detection information, which will make the feature representation learned by the target detection network not robust enough.
In infrared small sample target detection, this limitation will have a greater impact

Method used

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  • Infrared sample target detection method based on cyclic consistent adversarial network
  • Infrared sample target detection method based on cyclic consistent adversarial network
  • Infrared sample target detection method based on cyclic consistent adversarial network

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Embodiment Construction

[0038] The proposed method mainly includes: enhancement of infrared image details of the original data set, modification of image generation network, training of image generation network, yolov3 image detection and other steps. figure 1 A block diagram of the proposed method is given. include:

[0039] 1. Raw data set infrared image detail enhancement

[0040] This patent selects the FLIR ADAS data set as the original data set. The data images contained in the data set are infrared images and visible light images of non-corresponding scenes, and 1366 infrared images and 1257 visible light images are selected as training images. The details of the infrared image are enhanced, the specific method is as follows:

[0041] For the original infrared image, select a local window w with pixel k as the center and r as the radius k , using equations (1) and (2) to calculate a k , b k :

[0042]

[0043]

[0044] Denote the input raw infrared image by I, For the input origi...

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Abstract

The invention relates to an infrared sample target detection method based on a cyclic consistent adversarial network. The method comprises the following steps: 1) enhancing details of an infrared image of an original data set; modifying the image generation network; training an image generation network, training the adjusted image generation network by using the data set subjected to infrared image detail enhancement, the training data comprising a visible light image and an infrared image subjected to image detail enhancement processing; obtaining a final image generation network model; the method is applied to a target detection network, and a visible light image obtained through an image generation network model is input into a target detection model for target detection.

Description

technical field [0001] The invention belongs to the field of image processing and relates to an infrared small sample target detection method. Background technique [0002] The human visual system can only receive spectral information of visible light. Experts and scholars have proposed many advanced computer vision target detection methods in the field of visible light images. However, visible light images are easily affected by environmental factors such as weather and light intensity, and it is difficult to perform target detection tasks in certain environments. Infrared images represent the energy or temperature information of the detection target in the current scene by receiving thermal imaging of the target and background radiation, and the information is rarely affected by environmental factors. Therefore, infrared image target detection has become an important research direction. [0003] With the development of deep learning technology, infrared image target dete...

Claims

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Application Information

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IPC IPC(8): G06T7/00G06N3/04
CPCG06T7/0002G06T2207/10048G06T2207/20081G06N3/045
Inventor 杨嘉琛李爱云
Owner TIANJIN UNIV
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