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Image segmentation method and device, model training method and device and electronic equipment
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An image segmentation and image technology, applied in the field of image processing, can solve problems affecting detection accuracy and semantic segmentation difficulties
Pending Publication Date: 2022-02-08
重庆赛迪奇智人工智能科技有限公司
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For example, when using the RPN (Region Proposal Network, Region Proposal Network) in the deep learning algorithm and the two-stage detection algorithm of the subsequent classification and regression, the scale differences of various objects in the image and the interference factors such as water stains will bring negative effects to the semantic segmentation. It is difficult to affect the detection accuracy
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no. 1 example
[0054] Please refer to figure 1 , the present application provides a model training method, which can be applied to an electronic device, and each step in the method is executed or realized by the electronic device.
[0055] Understandably, an electronic device may include a processing module and a storage module. A computer program is stored in the storage module, and when the computer program is executed by the processing module, the electronic device can execute the steps in the following image segmentation method or model training method. Wherein, the electronic device may be, but not limited to, a personal computer, a server and other devices.
[0056] In this embodiment, the model training method may include the following steps:
[0057] Step S110, acquiring an image set, the image set includes a plurality of images with preset tags;
[0058] Step S120, performing image division on each training image in the image set to obtain a plurality of first image blocks;
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no. 2 example
[0096] Please refer to Figure 5 , the present application also provides an image segmentation method, which can be applied to the above-mentioned electronic device, and each step in the method is executed or realized by the electronic device.
[0097] The method may include the steps of:
[0098] Step S310, down-sampling the input first image to obtain a second image with a specified resolution, the specified resolution being lower than the resolution of the first image;
[0099] Step S320, through the tested deep neural network model, perform feature extraction on the first image to obtain a first-type image feature of a different size, and perform feature extraction on the second image through the deep neural network model Extract to obtain J second-class image features of different sizes, where I and J are integers greater than 0;
[0100] Step S330, through the deep neural network, perform feature enhancement on some or all of the image features of the I first-type imag...
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Abstract
The invention provides an image segmentation method and device, a model training method and device and electronic equipment. The method comprises the steps: carrying out the down-sampling of an input first image, and obtaining a second image with a specified resolution; performing feature extraction on the first image and the second image by using a deep neural network model to obtain I first-class image features with different sizes and J second-class image features with different sizes; through a deep neural network, performing feature enhancement on a part of or all image features in the I first-class image features and the J second-class image features, and fusing the enhanced image features to obtain fused image features; and the fused image features are segmented through the deep neural network model, and the segmentation result representing the first image is output, so that the detection precision of the small target can be improved while large and medium targets are considered, and the robustness and accuracy of the algorithm are further improved.
Description
technical field [0001] The present application relates to the technical field of image processing, in particular, to an image segmentation method, a model training method, a device, and electronic equipment. Background technique [0002] In the field of machine vision, the corresponding algorithms of machine vision can be used to classify various objects. For example, when using the RPN (Region Proposal Network, Region Proposal Network) in the deep learningalgorithm and the two-stage detection algorithm of the subsequent classification and regression, the scale differences of various objects in the image and the interference factors such as water stains will bring negative effects to the semantic segmentation. It is difficult to affect the detection accuracy. Contents of the invention [0003] The purpose of the embodiments of the present application is to provide an image segmentation method, a model training method, a device, and an electronic device, which can improve...
Claims
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