Full convolutional network semantic segmentation method based on multi-scale low-level feature fusion

Abstract

The invention discloses a full convolutional network (FCN) semantic segmentation method based on multi-scale low-level feature fusion. The method comprises firstly extracting the dense feature of an input image by using the FCN; and then subjecting the extracted feature images to multi-scale feature fusion, including steps of subjecting the input feature images to multi-scale pooling to form a plurality of processing branches; then performing low-level feature fusion on the pooled scale-invariant feature maps in respective branches and performing low-level feature fusion upsampling on the pooled scale-down feature maps in respective branches; inputting the feature maps into a 3*3 convolutional layer to learn deeper features and reduce the number of channels of output feature maps; then combining the output feature maps of respective branches in a channel number splicing manner, and obtaining a score map having a size the same as that of an original image by a class convolutional layerand bilinear interpolation upsampling. In combination with local low-level feature information and global multi-scale image information, the effect of image semantic segmentation is significant.

Description

technical field [0001] The invention relates to the technical fields of machine learning and computer vision, in particular to a full convolutional network semantic segmentation method based on fusion of multi-scale low-level features. Background technique [0002] In recent years, with the development of science and technology, the performance of computers has been rapidly improved, and the fields of machine learning, computer vision, and artificial intelligence have also developed rapidly. Image semantic segmentation has also become an important research topic. The so-called image semantic segmentation is to divide an image into several small blocks according to its own established standards. The pixels inside each small block have certain correlations, and the semantics of each block are marked, such as: sky, grassland , sofa, bed and more. From a technical point of view, image semantic segmentation is similar to aggregation in data processing to a certain extent, cluste...

AI Technical Summary

Problems solved by technology

Although these algorithms have expanded the receptive field of the fully convolutional neural network to a certain extent, it is easy to make the edge of the segmented object very rough, and it is incapable of recognizing small-scale objects.

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