A Real-time Semantic Segmentation Approach with Gated Multilayer Fusion

Abstract

The invention discloses a real-time semantic segmentation method of gated multi-layer fusion. Firstly, a gated multi-layer fusion network is constructed, and then a training set and a test set are used to train and test the gated multi-layer fusion network to obtain a prediction model for final segmentation. Finally, The real-time collected image is processed by using the prediction model of the final segmentation to obtain the final segmented image output. The present invention adopts a lightweight model as the main structure, and rationally uses 1×1 convolution to reduce channel dimensionality, and the finally designed model improves the running speed while ensuring the accuracy. The multi-layer fusion architecture realizes the fusion of different semantic features of different layers, which can improve the restoration of semantic information and greatly reduce the boundary smoothness of the predicted image. The gating structure weights down-sampling parallel layer semantic information, making the U-shaped connection more efficient, and promoting feedback and supervision between adjacent layers, low-level supervision and high-level semantic supplementation, and high-level downsampling of low-level.

Description

technical field [0001] The invention relates to the technical field of computer vision, in particular to a real-time semantic segmentation method of gated multi-layer fusion. Background technique [0002] Semantic segmentation has become a key technology in the field of computer vision. Semantic segmentation tasks can better obtain relevant information from computer scenes, so better solving semantic segmentation tasks can provide effective help for computer scene understanding. Applications include autonomous driving, medical image analysis, and human-computer interaction. Semantic segmentation can be defined as detecting the value of each pixel of an image, and then performing pixel-by-pixel comparison with the given label to accurately classify each pixel of the image. [0003] With the rapid development of deep learning, more and more deep learning algorithms are used to solve the semantic segmentation problem, which not only simplifies the channel for performing semant...

AI Technical Summary

Problems solved by technology

[0004] What the present invention is to solve is the problem that the existing semantic segmentation method loses a large number of effective features and model running speed when performing deep learning, and provides a real-time semantic segmentation method of gated multi-layer fusion, which can reduce the model complexity , can maintain a good prediction accuracy

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