Image segmentation method and device, electronic equipment and computer readable storage medium

Abstract

The embodiment of the invention discloses an image segmentation method and device, electronic equipment, a processor and a computer readable storage medium. The method comprises the steps that U-shaped expansion is conducted on a preset YOLOv3 model, an improved YOLOv3 model containing a U-shaped sampling unit is obtained, the U-shaped sampling unit comprises a down-sampling network and an up-sampling network, the down-sampling network is composed of a Darknet53 network of the YOLOv3 model, and the up-sampling network is formed by connecting a plurality of up-sampling modules in the YOLOv3 model in series; training the improved YOLOv3 model by using a sample image of a preset training sample set to obtain an image segmentation model; and performing image segmentation processing on the to-be-processed image by using the image segmentation model. According to the embodiment, the Darknet53 is adopted as the down-sampling network, the depth of the sampling network is large, the model expression ability is high, the whole model is easy to train, and then the method can be suitable for complex image segmentation scenes.

Description

technical field [0001] The present invention relates to the technical field of image processing, in particular to an image segmentation method, device, electronic equipment and computer-readable storage medium based on the improved YOLOv3 model. Background technique [0002] After the rise of deep learning, there have been techniques for image processing using convolutional neural networks, such as the U-Net model (such as figure 1 shown), YOLOv3 model (such as figure 2 shown) etc. The U-Net model belongs to a small image segmentation network. Since the U-Net model is not deep enough, if the model depth is increased by increasing the number of downsampling modules, there will be a risk of gradient disappearance, which will increase the difficulty of training. Therefore, U-Net The model is generally used in medical image segmentation (because medical image semantics are relatively simple and the structure is relatively fixed). In other words, the U-Net model is suitable fo...

AI Technical Summary

Problems solved by technology

For complex image segmentation tasks, there is currently no effective solution

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