A vehicle image semantic segmentation system based on bilateral segmentation network

Abstract

The invention discloses a vehicle-mounted image semantic segmentation system based on a bilateral segmentation network. The system comprises a data storage module, which is used for storing a vehicle-mounted image training set and a vehicle-mounted image to be tested; a bilateral segmentation network consisting of a spatial channel and a context channel, wherein the spatial channel is used to extract the spatial information of the vehicle image, and the context channel is used to extract the context semantic information of the vehicle image; a training module for training a bilateral segmentation network using a vehicle-mounted image training set; a semantic segmentation module used for predicting the vehicle image to be tested by using the trained bilateral segmentation network, and obtaining the class to which each pixel in the vehicle image to be tested belongs. The invention discloses a bilateral segmentation network comprising a spatial channel and a context channel, wherein the spatial channel is used for extracting spatial information of an image while retaining enough spatial information, and the context channel is used for extracting context semantic information of the image while ensuring a large enough receptive field.

Description

technical field [0001] The invention belongs to the technical field of pattern recognition, and more specifically relates to a vehicle-mounted image semantic segmentation system based on a bilateral segmentation network. Background technique [0002] Image semantic segmentation is one of the cornerstone problems in computer vision and can be widely used in scenarios such as augmented reality devices, autonomous driving, and video surveillance. Image semantic segmentation is to assign a semantic label to each pixel in the image, that is, to identify the category to which each pixel belongs, and to segment different objects at the same time. [0003] At present, the real-time image semantic segmentation algorithm mainly accelerates the model through the following three methods: 1) by pruning or adjusting the image, and then constraining the size of the input image, thereby reducing the computational complexity; 2) by pruning the number of channels of the network, In particula...

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Problems solved by technology

[0005] In view of the defects of the prior art, the purpose of the present invention is to solve the technical problems such as the lack of spatial information and the reduction of the receptive field existing in the real-time image semantic segmentation method of the prior art

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