FIELD OF THE TECHNOLOGY

This application relates to the field of computer technologies, and in particular, to training for an image semantic segmentation model.

BACKGROUND OF THE DISCLOSURE

An image semantic segmentation is the basis of image understanding, and the image semantic segmentation is very important in automatic drive, UAV applications, and wearable device applications. An image is formed by many pixels, and the semantic segmentation is grouping the pixels according to different semantic meanings expressed in the image.

In the related art, usually, a conventional deep convolutional neural network is trained as an image semantic segmentation network. First, full-image classification is performed on an input image, object regions corresponding to a full-image classification annotation in the image are located according to the network, and then the regions are used as supervision information for an image semantic segmentation, and the image semantic segmentation network is trained by using the supervision information.

In the related art, by locating the object regions corresponding to the full-image classification annotation by using a conventional convolution operation, usually, only one or more most discriminative parts of an entire object can be located, and it is difficult to locate all object regions. Therefore, the image semantic segmentation in the related art has a problem that not all object regions can be located.

SUMMARY

Embodiments of this application provide a training method for an image semantic segmentation model and a server, to locate all object regions from a raw image, thereby improving the segmentation quality of an image semantic segmentation.

To resolve the foregoing technical problem, the embodiments of this application provide the following technical solutions:

According to one aspect, an embodiment of this application provides a training method for an image semantic segmentation model, applied to a server, the method including:

obtaining a raw image used for model training;

performing a full-image classification annotation on the raw image at different dilation magnifications by applying a multi-magnification dilated convolutional neural network model to the raw image, and obtaining global object location maps in the raw image at different degrees of dispersion corresponding to the different dilation magnifications, wherein a degree of dispersion is used for indicating a distribution of a target object on an object region positioned by the multi-magnification dilated convolutional neural network model at a dilation magnification corresponding to the degree of dispersion; and

training an image semantic segmentation network model using the global object location maps as supervision information.

According to one aspect, an embodiment of this application provides a server, the server including: a processor, memory and a plurality of programs stored in the memory, wherein the plurality of programs, when executed by the processor, cause the server to perform the method according to any one of the first aspect.

According one aspect, an embodiment of this application provides a non-transitory computer readable storage medium, the computer readable storage medium storing instructions, the instructions, when executed by a processor of a server, cause the server to perform the method of the foregoing aspects.

It can be seen from the foregoing technical solutions that, the embodiments of this application have the following advantages:

In the embodiments of this application, first, a raw image used for model training is obtained, then, a full-image classification annotation is performed on the raw image at different dilation magnifications by using a multi-magnification dilated convolutional neural network model, to obtain global object location maps in the raw image at different degrees of dispersion, any degree of dispersion being used for indicating a distribution, on a target object, of object regions located at a dilation magnification corresponding to the degree of dispersion by the multi-magnification dilated convolutional neural network model; and finally, the global object location maps are used as supervision information for an image semantic segmentation network model, and the image semantic segmentation network model is trained by using the supervision information. In the embodiments of this application, because the full-image classification annotation is performed on the raw image by using the multi-magnification dilated convolutional neural network model, global object location maps at different degrees of dispersion can be located from the raw image through a multi-magnification dilated convolution of the multi-magnification dilated convolutional neural network model. The global object location maps include all regions of the target object. Therefore, in the embodiments of this application, all object regions corresponding to the full-image classification annotation in the raw image are precisely located by the multi-magnification dilated convolutional neural network model, thereby improving the segmentation quality of an image semantic segmentation.

