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Maritime unmanned aerial vehicle video image defogging method based on deep learning

A video image and deep learning technology, which is applied in image enhancement, image analysis, image data processing, etc., can solve problems such as blurred video images, achieve good visual effects, clear video images, and reduce unknown parameters.

Active Publication Date: 2019-07-05
浙江欣挪瑞海洋科技有限公司
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Problems solved by technology

Therefore, it is necessary to propose a computer video image enhancement technology to solve the problem of blurred video images collected by drones under foggy conditions.

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  • Maritime unmanned aerial vehicle video image defogging method based on deep learning
  • Maritime unmanned aerial vehicle video image defogging method based on deep learning
  • Maritime unmanned aerial vehicle video image defogging method based on deep learning

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Embodiment Construction

[0029] The present invention will be further described in detail below in conjunction with the accompanying drawings and specific embodiments.

[0030] Such as figure 1 As shown, the specific steps of a kind of deep learning-based maritime UAV video image defogging method proposed by the present invention are as follows:

[0031] 1) The marine video images are captured by the UAV onboard camera, and according to the atmospheric scattering model, the clear video images of the same scene are synthesized into foggy video images of different concentrations, and a marine video image database is established. The specific formula of the atmospheric scattering model is as follows:

[0032] I(x)=J(x)t(x)+A(1-t(x))

[0033] Among them, I(x) represents the foggy image, J(x) represents the clear image, t(x) represents the transmittance, and A represents the atmospheric light value in the air. In order to ensure that the synthesized foggy image is more realistic, the value range of A is...

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Abstract

The invention discloses a maritime unmanned aerial vehicle video image defogging method based on deep learning, and the method comprises the steps of 1) shooting an offshore clear video image throughan unmanned aerial vehicle airborne camera, and building an offshore video image database according to an atmospheric scattering model; 2) establishing a multi-scale convolutional neural network model, and training by using an offshore video image database; 3) acquiring an offshore video image through a visible light camera mounted on the unmanned aerial vehicle to obtain a shot foggy image I '(x); 4) inputting the foggy image I '(x) into the trained multi-scale convolutional neural network model for processing to obtain an intermediate variable k (x); 5) obtaining a final defogged image J '(x) by utilizing a clear image restoration formula, wherein the clear image restoration formula is J' (x) = k (x) (I '(x)-1), I '(x) is the shot foggy video image, J' (x) is a restored clear image, k (x) is an intermediate variable, and b can take any constant. The method can effectively solve the problem of blurring of the video images obtained by the unmanned aerial vehicle visual system under thefoggy weather condition, thereby improving the search and rescue capability of the unmanned aerial vehicle.

Description

technical field [0001] The invention relates to the technical field of computer video image enhancement, in particular to a method for defogging video images of maritime drones based on deep learning. Background technique [0002] The frequent occurrence of marine traffic accidents seriously endangers the life and property safety of the crew and passengers. Due to the particularity of marine vehicles and the complexity of the navigation environment, it is very important to search and rescue people in distress at the first time. Due to its fast speed, wide search range, and autonomous navigation capabilities, maritime drones can realize functions such as environment perception, target recognition, and tracking, and play a positive and significant role in the search and rescue of people in distress at sea. [0003] However, drones are often affected by harsh natural environments when performing search and rescue missions at sea. In foggy conditions, the video images taken by...

Claims

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Application Information

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IPC IPC(8): G06T5/00
CPCG06T2207/10016G06T2207/20081G06T2207/20084G06T5/73
Inventor 刘文殷伟杨梅芳聂鑫
Owner 浙江欣挪瑞海洋科技有限公司
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