Real-time high-performance street-view image semantic segmentation method based on deep learning

Abstract

The invention discloses a real-time high-performance street-view image semantic segmentation method based on deep learning. The real-time high-performance street-view image semantic segmentation method includes the steps: preparing a street-view image training, verifying and testing data set; carrying out downsampling on images of the data set to reduce the resolution of the images; transforming an existing lightweight classification network to serve as a basic feature extraction network of semantic segmentation; connecting identification hole space pyramid pooling in series after the basic feature extraction network for solving the multi-scale problem of semantic segmentation; stacking a plurality of convolutional layers to form a shallow spatial information storage network; fusing the obtained feature maps by using a feature fusion network to form a prediction result; comparing the output image with a semantic annotation image in the data set, and performing end-to-end training by using a back propagation algorithm to obtain a real-time high-performance street-view image semantic segmentation network model; and inputting the street-view image to be tested into the real-time high-performance street-view image semantic segmentation network model to obtain a semantic segmentation result of the street-view image.

Description

technical field [0001] The invention relates to computer vision technology, in particular to a method for semantic segmentation of real-time high-performance street view images based on deep learning. Background technique [0002] Semantic segmentation is one of the tasks of scene understanding. It provides detailed pixel-level classification and is a very basic but very challenging task in the field of computer vision. Semantic segmentation can be widely used in various real-world scenarios, such as unmanned driving, robots, or augmented reality, etc. These applications have a strong demand for semantic segmentation algorithms. [0003] Early semantic segmentation used methods based on manual features, such as random forests, etc., but the effects of these methods were not ideal. In recent years, with the continuous development of deep learning technology, deep convolutional neural networks have been widely used in various computer vision tasks, such as image classificatio...

AI Technical Summary

Problems solved by technology

At present, the work of improving computational efficiency mainly focuses on image classification and target tracking, and there are relatively few studies on fast semantic segmentation.

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