Indoor scene semantic segmentation method based on improved full convolutional neural network

Abstract

The invention discloses an indoor scene semantic segmentation method based on an improved full convolutional neural network. The method comprises the steps of firstly constructing a convolutional neural network, wherein a hidden layer of the convolutional neural network comprises five neural network blocks, five feature re-extraction convolutional layer blocks, five partitioning attention convolutional blocks, twelve fusion layers and four up-sampling layers; 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 original indoor scene images and a set formed by one-hot coded images processed by corresponding real semantic segmentation images to obtain an optimal weight vector and an offset term of a convolutional neural network classification training model; and inputtingan indoor scene image to be subjected to semantic segmentation into the trained convolutional neural network classification training model to obtain a predicted semantic segmentation image. The methodhas the advantage that the semantic segmentation efficiency and accuracy of the indoor scene image are improved.

Description

technical field [0001] The invention relates to a deep learning semantic segmentation method, in particular to an indoor scene semantic segmentation method based on an improved fully convolutional neural network. Background technique [0002] Semantic image segmentation is one of the most challenging tasks in computer vision and plays a key role in applications such as autonomous driving, medical image analysis, virtual reality, human-computer interaction, etc. The core purpose of semantic segmentation is to give a category label for each pixel in a picture, and determine which category the pixel belongs to. Since the dataset of semantic segmentation generally involves indoor scenes or outdoor scenes, there are many segmented objects, so in It is essentially a multi-classification problem. [0003] From the perspective of supervised learning, image semantic segmentation can be divided into three types: fully supervised, semi-supervised and unsupervised. However, from the pe...

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Problems solved by technology

[0004] In terms of model application, based on the emergence and development of fully convolutional neural networks, the application of fully convolutional neural networks has achieved superior performance and segmentation results in

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