Road scene semantic segmentation method based on full residual cavity convolutional neural network

Abstract

The invention discloses a road scene semantic segmentation method based on a full-residual cavity convolutional neural network. The method comprises the steps: constructing the full-residual cavity convolutional neural network at a training stage, and enabling the full-residual cavity convolutional neural network to comprise an input layer, a hidden layer and an output layer, and enabling the hidden layer to comprise one transition convolution block, eight neural network blocks, seven deconvolution blocks and four fusion layers; inputting each original road scene image in the training set intoa full residual cavity convolutional neural network for training to obtain 12 semantic segmentation prediction images corresponding to each original road scene image; calculating a loss function value between a set formed by 12 semantic segmentation prediction images corresponding to each original road scene image and a set formed by 12 one-hot coded images processed by corresponding real semantic segmentation images to obtain a full residual cavity convolutional neural network training model; in the test stage, a full residual cavity convolutional neural network training model is used for prediction; the method has the advantages of high segmentation accuracy and strong robustness.

Description

Technical field [0001] The invention relates to a semantic segmentation method for deep learning, in particular to a road scene semantic segmentation method based on a full residual cavity convolutional neural network. Background technique [0002] The rise of the intelligent transportation industry has made semantic segmentation more and more applications in intelligent transportation systems. From traffic scene understanding and multi-object obstacle detection to visual navigation can all be realized by semantic segmentation technology. At present, the most commonly used semantic segmentation methods include support vector machines, random forests and other algorithms. These traditional machine learning methods mainly focus on binary classification tasks, which are used to detect and recognize specific objects, such as road surfaces, vehicles and pedestrians, etc., and often require high-complexity features to achieve. [0003] The semantic segmentation method of deep learning d...

AI Technical Summary

Problems solved by technology

At present, the road scene semantic segmentation method based on deep learning uses many models that combine convolutional layers and pooling layers. However, the feature maps obtained by simply using pooling operations and convolution operations are single and unrepresentative, which will lead to The feature information of the obtained image is reduced, which will eventually lead to rough restoration of the effect information and low segmentation accuracy.

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