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Road scene semantic segmentation method based on convolutional neural network

A convolutional neural network and semantic segmentation technology, applied in the field of semantic segmentation of deep learning, can solve the problems of image feature information reduction, low segmentation accuracy, and non-representative

Inactive Publication Date: 2021-03-16
ZHEJIANG UNIVERSITY OF SCIENCE AND TECHNOLOGY
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AI Technical Summary

Problems solved by technology

[0004] Most of the existing road scene semantic segmentation methods use deep learning methods. There are many models that combine convolutional layers and pooling layers. However, the feature maps obtained by purely using pooling operations and convolution operations are single and not representative. , which will lead to the reduction of the feature information of the obtained image, which will eventually lead to rough restoration of the effect information and low segmentation accuracy.

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  • Road scene semantic segmentation method based on convolutional neural network
  • Road scene semantic segmentation method based on convolutional neural network
  • Road scene semantic segmentation method based on convolutional neural network

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Embodiment Construction

[0042] The present invention will be further described in detail below in conjunction with the accompanying drawings and embodiments.

[0043] A method for road scene semantic segmentation based on convolutional neural network proposed by the present invention, its overall realization block diagram is as follows figure 1 As shown, it includes two processes of training phase and testing phase;

[0044] Described step 1_1 is specifically:

[0045] Select Q original road scene images and the real semantic segmentation images corresponding to each original road scene image, and select the qth original road scene image as The heat map corresponding to the original road scene image is denoted as Using the HHA coding method to process the thermal image into three channels and superimpose it with the original road scene image to form a color thermal image, which is recorded as Record the real semantic segmentation image corresponding to the qth original road scene image as

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Abstract

The invention discloses a road scene semantic segmentation method based on a convolutional neural network. The method includes: n a training stage, building a convolutional neural network, and a hidden layer of the convolutional neural network comprises ten neural network blocks, three convolutional blocks, two joint pyramid up-sampling modules and separable up-sampling blocks; inputting the original road scene image into a convolutional neural network for training to obtain nine corresponding semantic segmentation prediction images; calculating a loss function value between a set formed by nine semantic segmentation prediction images corresponding to an original road scene image and a corresponding semantic segmentation label image set to obtain an optimal weight vector and an offset termof a convolutional neural network classification training model; in a test stage, inputting a road scene image to be semantically segmented into the convolutional neural network classification training model to obtain a predicted semantic segmentation image. According to the invention, the semantic segmentation efficiency of the road scene image is improved, and the accuracy is improved.

Description

technical field [0001] The present invention relates to a deep learning semantic segmentation method, in particular to a convolutional neural network-based semantic segmentation method for road scenes. Background technique [0002] The rise of the intelligent transportation industry has led to more and more applications of semantic segmentation in intelligent transportation systems. From traffic scene understanding and multi-target obstacle detection to visual navigation, semantic segmentation technology can be used to achieve. Currently, the most commonly used semantic segmentation methods include algorithms such as support vector machines and random forests. These algorithms mainly focus on binary classification tasks to detect and recognize specific objects such as road surfaces, vehicles, and pedestrians. These traditional machine learning methods often need to be implemented through high-complexity features, but it is simple and convenient to use deep learning to seman...

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Application Information

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IPC IPC(8): G06T7/10G06T9/00G06N3/04G06N3/08
CPCG06T7/10G06T9/002G06N3/08G06T2207/20081G06T2207/20084G06T2207/30256G06T2207/10024G06N3/045
Inventor 周武杰刘劲夫钱小鸿雷景生万健杨胜英强芳芳
Owner ZHEJIANG UNIVERSITY OF SCIENCE AND TECHNOLOGY
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