Traffic image semantic segmentation method based on multi-feature map

Abstract

The invention discloses a traffic image semantic segmentation method based on a multi-feature map. The method comprises the following steps: obtaining a multi-feature map training sample: a disparitymap, a height map and an angle map; constructing a network model, training the network model, inputting the trained network model and a six-channel test image into the network model, outputting a probability value that each pixel belongs to each object category in the six-channel image via a multi-class classifier softmax layer, then predicting the object category to which each pixel in the six-channel image belongs, and finally outputting an image semantic segmentation map. By adoption of the traffic image semantic segmentation method based on the multi-feature map provided by the invention,the fusion of a color image with a depth map, the height map and the angle map, more feature information of the image can be obtained, and it is conducive to understanding the road traffic scene and improving the semantic segmentation accuracy. According to the traffic image semantic segmentation method based on the multi-feature map provided by the invention, by means of the learned effective features, the object category to which each pixel in the image belongs can be predicted, and the image semantic segmentation map is output.

Description

technical field [0001] The invention relates to the fields of computer vision and pattern recognition, in particular to a method for semantic segmentation of multi-feature map traffic images based on stereo vision and a deep convolutional neural network model. Background technique [0002] The understanding and perception of traffic scenes is of great significance in obstacle detection, traversability estimation, and path planning of smart cars. However, the road traffic scene has the characteristics of complexity, variability and uncertainty, which makes the perception and understanding of the traffic scene difficult and the accuracy is low. Before the emergence of deep convolutional neural networks, the perception and understanding of traffic scenes were mostly realized by manually designing feature extractors to classify traffic scenes, which needed to be realized by writing complex programs, and were mainly effective for specific categories. universal. [0003] In rece...

AI Technical Summary

Problems solved by technology

However, the current semantic segmentation method for traffic images based on RGB images fails to make full use of the feature information of the image, resulting in low semantic segmentation accuracy.

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