Semantic segmentation method based on residual pyramid pooling neural network

Abstract

The invention discloses a semantic segmentation method based on a residual pyramid pooling neural network. The method comprises the following steps: firstly, constructing a convolutional neural network, wherein a hidden layer of the convolutional neural network comprises 10 residual neural network blocks, 4 Residual ASPP blocks and 5 Basic blocks; inputting the original indoor scene image into a convolutional neural network for training to obtain a corresponding semantic segmentation prediction graph; calculating a loss function value between a set formed by semantic segmentation prediction images corresponding to the original indoor scene images and a set formed by 40 single-heat coded images processed by corresponding real semantic segmentation images to obtain an optimal weight vector and a bias term of a convolutional neural network classification training model; and in a test stage, inputting an indoor scene image to be semantically segmented into the convolutional neural networkclassification training model to obtain a semantic segmentation prediction graph. According to the invention, the semantic segmentation efficiency and accuracy of the indoor scene image are improved.

Description

technical field [0001] The invention is a semantic segmentation method based on a fully convolutional neural network, especially an indoor scene semantic segmentation method for residual pyramid pooling. Background technique [0002] Semantic segmentation is a fundamental technique for many computer vision applications, such as scene understanding, autonomous driving. With the development of Convolutional Neural Networks, especially Fully Convolutional Neural Networks (FCNs), many promising results have been achieved on benchmarks. FCN has a typical encoder-decoder structure—semantic information is first embedded into the feature map through the encoder, and the decoder is responsible for generating segmentation results. Typically, the encoder is a pre-trained convolutional model to extract image features, and the decoder contains multiple upsampling components to recover resolution. Although the encoder's most important feature map may be highly semantic, it has limited a...

AI Technical Summary

Problems solved by technology

Although the encoder's most important feature map may be highly semantic, it has limited ability to reconstruct precise details in the segmentation map due to insufficient resolution, which is very common in modern backbone models.

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