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A Semantic Segmentation Method for Panoramic Water Image

A panoramic image and semantic segmentation technology, applied in the field of computer vision recognition, can solve problems such as slow speed and poor segmentation of small-area targets, and achieve the effects of avoiding gradient attenuation, improving network segmentation accuracy, and improving segmentation accuracy

Active Publication Date: 2021-10-08
HUAZHONG UNIV OF SCI & TECH
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  • Application Information

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Problems solved by technology

[0006] Aiming at the above defects or improvement needs of the prior art, the present invention provides a method for semantic segmentation of panoramic water surface images, thereby solving the technical problems existing in the prior art that the speed is slow and the segmentation effect on small area objects is not good

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  • A Semantic Segmentation Method for Panoramic Water Image
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  • A Semantic Segmentation Method for Panoramic Water Image

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Embodiment 1

[0038] A method for semantic segmentation of water surface panoramic images, comprising:

[0039]Input the panoramic image of the water surface to be tested into the convolutional neural network for real-time semantic segmentation, and obtain the segmentation result of the panoramic image of the water surface;

[0040] like figure 2 As shown in (a), the traditional convolutional neural network uses a convolution with a length and a width of 3, and the size of the traditional convolution kernel is 3*3. The present invention uses two sizes of 3*1 and 1*3 respectively. The convolution kernel, such as figure 2 As shown in (b), the convolutional neural network sets skip connections every 4 convolutional layers.

[0041] When the number of input convolutional layer channels is c1 and the number of output volume base layer channels is c2, the calculation amount of traditional convolution is:

[0042] 3*3*c1*c2=9*c1*c2

[0043] The convolution calculation amount of the present i...

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Abstract

The invention discloses a method for semantic segmentation of water surface panoramic images, comprising: designing a convolutional neural network, performing pixel-level marking on each target category in a water surface panoramic image in a training set to obtain a real marked image, using the training set to train the convolutional neural network, Compare the output of the convolutional neural network with the real labeled image to obtain the training error, backpropagate the training error in the convolutional neural network, update the parameters of the convolutional neural network, and get the trained one after multiple iterations of training Convolutional neural network. The water surface panoramic image to be tested is input into the convolutional neural network for real-time semantic segmentation, and the segmentation result of the water surface panoramic image is obtained. The invention has fast segmentation speed and good segmentation effect on small area targets. Provide comprehensive, fast and accurate environmental perception information for surface smart devices such as unmanned boats.

Description

technical field [0001] The invention belongs to the technical field of computer vision recognition, and more specifically relates to a method for semantic segmentation of water surface panoramic images. Background technique [0002] With the proposal of the ocean power strategy, my country has begun to vigorously develop marine equipment. As an unmanned surface mobile platform, unmanned boats can not only enter some harsh environments to complete tasks, but also improve the efficiency of tasks that require long-term operations. They play a very important role in many practical applications, such as Customs patrols, shallow sea mine clearance and water quality monitoring, etc. For unmanned boats, environmental perception technology is an indispensable part of autonomous navigation and autonomous obstacle avoidance. In particular, in the process of actual operation, it is far from enough to only be able to detect targets or obstacles in the direction of advancement, and dang...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06K9/00G06K9/34G06K9/62G06N3/04G06N3/08
CPCG06N3/084G06V20/00G06V10/267G06V2201/07G06N3/045G06F18/254G06F18/214
Inventor 曹治国李德辉肖阳朱昂帆赵晨杨健宫凯程
Owner HUAZHONG UNIV OF SCI & TECH
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