Image segmentation method and device based on multi-path convolutional neural network model
A convolutional neural network and image segmentation technology, applied in the field of deep learning, can solve the problems of image segmentation methods such as difficult image segmentation, and achieve the effect of improving performance
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Embodiment 1
[0046] The following describes an image segmentation method based on a multi-path convolutional neural network model provided in Embodiment 1 of the present application. Please refer to the attached figure 1 , the image segmentation method based on the multi-path convolutional neural network model in Embodiment 1 of the present application includes:
[0047] Step S101, obtaining the first feature image and the second feature image of the image to be segmented, inputting the first feature image into the first path of the trained multi-path convolutional neural network model, and inputting the second feature image into the The second path of the model described above;
[0048] When the image needs to be segmented, the first characteristic image and the second characteristic image of the image to be segmented are obtained, and the specific categories of the first characteristic image and the second characteristic image can be set according to actual conditions.
[0049] After th...
Embodiment 2
[0117] Embodiment 2 of the present application provides an image segmentation device based on a multi-path convolutional neural network model. For the convenience of description, only the parts related to the present application are shown, such as figure 2 As shown, the image segmentation device based on the multi-path convolutional neural network model includes,
[0118] The feature extraction module 201 is used to obtain the first feature image and the second feature image of the image to be segmented, input the first feature image into the first path of the trained multi-path convolutional neural network model, and input the second feature image into the trained multi-path convolutional neural network model. a second path for inputting feature images into said model;
[0119] The target segmentation module 202 is used to use the segmentation result output by the main output fully connected layer in the model as the target segmentation result;
[0120] Wherein, the model i...
Embodiment 3
[0137] image 3 It is a schematic diagram of a terminal device provided in Embodiment 3 of the present application. Such as image 3 As shown, the terminal device 3 in this embodiment includes: a processor 30 , a memory 31 , and a computer program 32 stored in the memory 31 and operable on the processor 30 . When the processor 30 executes the computer program 32, it realizes the steps in the embodiment of the image segmentation method based on the multi-path convolutional neural network model, for example figure 1 Steps S101 to S102 are shown. Alternatively, when the processor 30 executes the computer program 32, it realizes the functions of the modules / units in the above-mentioned device embodiments, for example figure 2 The functions of modules 201 to 202 are shown.
[0138] Exemplarily, the computer program 32 can be divided into one or more modules / units, and the one or more modules / units are stored in the memory 31 and executed by the processor 30 to complete this a...
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