Semantic segmentation method based on feature pyramid attention and mixed attention cascading

Abstract

The invention discloses a semantic segmentation method based on feature pyramid attention and mixed attention cascading. The method comprises the steps of building a semantic segmentation training set; constructing a deep convolutional neural network, wherein the deep convolutional neural network comprises an encoder part, two feature pyramid attention modules, a mixed attention module, a decoding branch, a feature fusion part and a deep separable convolutional layer; training the deep convolutional neural network by using the semantic segmentation training set, and correcting network parameters; and inputting the streetscape road scene image to be segmented into the trained deep convolutional neural network to obtain a segmentation result. The invention can better adapt to the requirements of unmanned vehicle equipment for precision and speed.

Description

technical field [0001] The invention belongs to pattern recognition technology, specifically a semantic segmentation method based on feature pyramid attention and mixed attention cascade. Background technique [0002] Image semantic segmentation (semantic segmentation), also known as scene parsing (scene parsing), is a basic and challenging research direction in computer vision at present. Its task is to assign semantic labels to each pixel in the image, and a scene image Segment and parse into distinct image regions that correspond to semantic categories, including continuous objects (e.g. sky, road, grass) and discrete objects (e.g. people, cars, bicycles), etc. [0003] Image semantic segmentation technology enables computers to understand complex images containing multi-category objects. Research in this area has a wide range of application values ​​in areas such as unmanned vehicles, robot perception, and medical images. In recent years, due to the emergence of GPU com...

AI Technical Summary

Problems solved by technology

Existing fast semantic segmentation algorithms usually only retain a simple codec structure for image feature extraction and restoration, and lack of full use of multi-scale feature information, resulting in low segmentation accuracy, especially for edge detail processing of segmentation targets Poor, it is difficult to meet the requirements for segmentation accuracy in practical applications

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