Road scene semantic segmentation method based on convolutional neural network

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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