US 12,260,556 B2
Image segmentation method, system, and device, and readable storage medium
Li Wang, Jiangsu (CN); Zhenhua Guo, Jiangsu (CN); Nan Wu, Jiangsu (CN); and Yaqian Zhao, Jiangsu (CN)
Assigned to INSPUR SUZHOU INTELLIGENT TECHNOLOGY CO., LTD., Jiangsu (CN)
Appl. No. 17/801,332
Filed by INSPUR SUZHOU INTELLIGENT TECHNOLOGY CO., LTD., Jiangsu (CN)
PCT Filed Oct. 27, 2020, PCT No. PCT/CN2020/124074
§ 371(c)(1), (2) Date Aug. 22, 2022,
PCT Pub. No. WO2021/164299, PCT Pub. Date Aug. 26, 2021.
Claims priority of application No. 202010104946.8 (CN), filed on Feb. 20, 2020.
Prior Publication US 2023/0083518 A1, Mar. 16, 2023
Int. Cl. G06T 7/11 (2017.01)
CPC G06T 7/11 (2017.01) [G06T 2207/20084 (2013.01)] 11 Claims
OG exemplary drawing
 
1. An image segmentation method, comprising:
obtaining an original image set;
performing feature extraction on the original image set by using a backbone network to obtain a feature map set;
performing channel extraction fusion processing on the feature map set by using a channel extraction fusion model to obtain an enhanced feature map set; and
segmenting the enhanced feature map set by using a preset convolutional neural network to obtain an image segmentation result,
wherein when the channel extraction fusion model comprises a first channel extraction fusion sub-model, the performing channel extraction fusion processing on the feature map set by using the channel extraction fusion model to obtain the enhanced feature map set comprises:
starting a first channel selection process of the first channel extraction fusion sub-model to perform channel selection on the feature map set to obtain a first enhanced feature map subset;
starting a first feature extraction process of the first channel extraction fusion sub-model to perform feature extraction on the feature map set to obtain a second enhanced feature map subset; and
superimposing the first enhanced feature map subset and the second enhanced feature map subset to obtain the enhanced feature map set,
wherein the starting the first channel selection process of the first channel extraction fusion sub-model to perform channel selection on the feature map set to obtain the first enhanced feature map subset comprises:
calculating a feature vector of each feature map in the feature map set;
calculating a weight value of each feature map by using an activation function according to each feature vector;
sorting each weight value from large to small, and selecting a preset proportion of top-ranking weight values as enhanced weight values; and
selecting a channel corresponding to each feature map according to a channel position corresponding to each enhanced weight value, and performing multiplication to obtain the first enhanced feature map subset,
wherein the starting the first feature extraction process of the first channel extraction fusion sub-model to perform feature extraction on the feature map set to obtain the second enhanced feature map subset comprises:
reading the weight value of each feature map, and determining an extraction probability of each channel according to each weight value;
extracting a preset proportion of channels as enhanced channels according to the extraction probability of each channel, and determining feature maps corresponding to the enhanced channels as enhanced feature maps;
performing feature extraction on each enhanced feature map to obtain a feature vector;
calculating a weight value of each enhanced feature map by using an activation function according to each feature vector; and
selecting a channel corresponding to each enhanced feature map according to a channel position corresponding to each weight value of each enhanced feature map, and performing multiplication to obtain the second enhanced feature map subset.