Yellow River ice semantic segmentation method based on multi-attention mechanism double-flow fusion network

A technology that integrates network and semantic segmentation. It is applied in neural learning methods, biological neural network models, computer components, etc. It can solve problems such as poor accuracy, and achieve improved segmentation accuracy, good segmentation effect, and good attention to detail information. Effect

Active Publication Date: 2020-05-15
NORTHWESTERN POLYTECHNICAL UNIV
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Problems solved by technology

[0004] In order to overcome the shortcomings of the poor accuracy of existing Yellow River ice detection methods, the present invention provides a semantic segmentation method for Yellow River ice based on a multi-attention mechanism and a two-stream fusion network

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  • Yellow River ice semantic segmentation method based on multi-attention mechanism double-flow fusion network
  • Yellow River ice semantic segmentation method based on multi-attention mechanism double-flow fusion network
  • Yellow River ice semantic segmentation method based on multi-attention mechanism double-flow fusion network

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Embodiment Construction

[0018] refer to Figure 1-5 . The specific steps of the Semantic Segmentation Method for the Yellow River Ice Snow Based on the Multi-Attention Mechanism Two-stream Fusion Network of the present invention are as follows:

[0019] 1. Prepare and build the dataset.

[0020] The data sets were taken by different UAVs on the Ningxia-Inner Mongolia section of the Yellow River. After manual selection, the data sets were marked, and the images were marked pixel by pixel. The marks were divided into three categories, including ice, water, and shore. Divide the marked data set into training set, verification set and test set according to the proportion of shooting time. The image size is 1600×640, the training set contains 570 images, the validation set contains 82 images, and the test set contains 244 images.

[0021] 2. Build a semantic segmentation network.

[0022] The network includes two branches, the shallow branch and the deep branch, and in order to better fuse the feature...

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Abstract

The invention discloses a Yellow River ice semantic segmentation method based on a multi-attention mechanism double-flow fusion network. The method is used for solving the technical problem that an existing Yellow River ice detection method is poor in accuracy. According to the technical scheme, firstly, data sets are collected and labeled, the labeled data sets are divided into a training data set and a test data set, then a segmentation network structure is constructed, the network comprises shallow branches and deep branches, and a channel attention module is added to the deep branch, a position attention module is added to the shallow branch, the fusion module is used for fusing the shallow branches and the deep branches, the data in the training set is added into the network in batches, the constructed neural network is trained by adopting cross entropy loss and an RMSprop optimizer, and finally, a to-be-tested image is input and a test is carried out by using the trained model. According to the method, multi-level and multi-scale feature fusion can be selectively carried out, context information is captured based on an attention mechanism, a feature map with higher resolutionis obtained, and a better segmentation effect is obtained.

Description

technical field [0001] The invention relates to a method for detecting Yellow River icicles, in particular to a method for semantic segmentation of Yellow River icicles based on a multi-attention mechanism and dual-stream fusion network. Background technique [0002] Semantic segmentation is a very important field in computer vision. It refers to the recognition of images at the pixel level, that is, to mark the object category to which each pixel in the image belongs, and its goal is to predict the class label of each pixel in the image. River ice monitoring is of great significance to river management in the shipping industry. Accurate ice segmentation is one of the most important techniques in ice regime monitoring research. UAV aerial images have the advantages of high definition, large scale, small area, and high visibility. It can provide prerequisite information for calculating ice sheet density, drift ice velocity, ice sheet distribution, change detection, etc. At...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/00G06K9/34G06K9/62G06N3/04G06N3/08
CPCG06N3/08G06V20/182G06V10/267G06N3/047G06N3/048G06N3/045G06F18/241G06F18/2415
Inventor 张秀伟张艳宁兰泽泽金娇娇余欣范旻昊李春江王亚飞
Owner NORTHWESTERN POLYTECHNICAL UNIV
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