Image scene segmentation method, device, computing device and computer storage medium

Abstract

The invention discloses an image scene segmentation method, a device, a computing device and a computer storage medium. The image scene segmentation method is executed on the basis of a well trained scene segmentation network. The method comprises the following steps of acquiring a to-be-segmented image; inputting the to-be-segmented image into a scene segmentation network, wherein at least one layer of a convolution layer is formed in the scene segmentation network; subjecting the first convolution block of the convolution layer to scaling treatment by means of a scale coefficient outputted by a scale regression layer to obtain a second convolution block; carrying out the convolution operation of the convolution layer by utilizing the second convolution block to obtain the output result of the convolution layer, wherein the scale regression layer is a middle convolution layer of the scene segmentation network; and outputting a scene segmentation result corresponding to the to-be-segmented image. According to the technical scheme, the self-adaptive scaling of a sensing field is realized. The scene segmentation result can be quickly obtained through the trained scene segmentation network, and the image scene segmentation accuracy and the processing efficiency are improved.

Description

technical field [0001] The present invention relates to the technical field of image processing, in particular to an image scene segmentation method, device, computing equipment and computer storage medium. Background technique [0002] In the existing technology, image scene segmentation processing methods are mainly based on fully convolutional neural networks in deep learning. These processing methods use the idea of ​​transfer learning to migrate the network obtained through pre-training on large-scale classification data sets to image processing methods. The segmentation network is trained on the segmentation data set to obtain the segmentation network for scene segmentation, and then the segmentation network is used to segment the scene of the image. [0003] The network architecture used in the segmentation network obtained in the prior art directly uses the image classification network, and the size of the convolution block in the convolutional layer is fixed, so the...

AI Technical Summary

Problems solved by technology

However, for image scene segmentation, the scene often contains objects of different sizes. Using a segmentation network with a fixed-size receptive field often causes problems when dealing with too large and too small objects. For example, for smaller objects, the receptive field It will capture too much background around the target, thereby confusing the target with the background, causing the target to be missed and misjudged as the background; for larger targets, the receptive field can only capture a part of the target, which makes the target category judgment biased, resulting in Discontinuous Segmentation Results

Therefore, the image scene segmentation processing method in the prior art has the problem of low accuracy of image scene segmentation

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