Urban road scene semantic segmentation method based on deep learning

Abstract

The invention discloses an urban road scene semantic segmentation method based on deep learning. The method comprises the following steps: 1) collecting an image at the front end of a vehicle; 2) input data expansion of the annotated image and the original image: randomly cutting, splicing or adding different types of noise to the image, transforming the image through an image affine matrix, and finally maintaining the original resolution of the image through filling, cutting and other transformation to obtain a data set; 3) training a network by using the image after data expansion and the annotation image, wherein the residual U-net network comprises a down-sampling part, a bridge part, an up-sampling part and a classification part; and 4) modifying the time interval T of the acquisitionmodule, inputting subsequently obtained images into the trained deep learning model, outputting predicted semantic segmentation images, and returning different gray levels in the images to the processor. A small data set is used, too fast gradient descent can be prevented, and it can be ensured that an over-fitting problem does not occur during training.

Description

technical field [0001] The invention belongs to the field of intelligent vehicles and relates to a semantic segmentation method for urban road scenes based on deep learning. Background technique [0002] In recent years, with the continuous development of urbanization, urban road conditions have become more and more complex. Pedestrians, traffic lights, zebra crossings and different means of transportation will all affect the speed and obstacle avoidance measures of smart vehicles. The semantic segmentation method through deep learning can well identify the environment around the vehicle and make different feedbacks. Semantic segmentation is to assign a preset category to each pixel of an image, which not only keeps intelligent vehicles understanding the surrounding environment in real time while driving, but also reduces the occurrence of traffic accidents. Therefore, the research on deep learning of urban road environment has always been a research hotspot in the field of...

AI Technical Summary

Problems solved by technology

[0003] In order to overcome the deficiencies of the existing technology and consider that intelligent vehicles can better identify the surrounding environment in complex environments such as urban roads, the present invention proposes a method for semantic segmentation of urban road scenes based on deep learning. Using a smaller data set can prevent the gradient from descending too fast, and can ensure that no overfitting occurs during training

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