System and method for semantic segmentation using dense upsampling convolution (DUC)
AssigneeTusimple, Inc.
InventorsZehua Huang, Pengfei Chen, Panqu Wang
Life of this patent
Estimating the term…
Abstract
A system and method for semantic segmentation using dense upsampling convolution (DUC) are disclosed. A particular embodiment includes: receiving an input image; producing a feature map from the input image; performing a convolution operation on the feature map and reshape the feature map to produce a label map; dividing the label map into equal subparts, which have the same height and width as the feature map; stacking the subparts of the label map to produce a whole label map; and applying a convolution operation directly between the feature map and the whole label map without inserting extra values in deconvolutional layers to produce a semantic label map.
Claims18
A system comprising:
- a data processor;
- and
- an image processing module, executable by the data processor, the image processing module being configured to perform semantic segmentation using a dense upsampling convolution (DUC) operation, the DUC operation being configured to:
- receive an input image;
- produce a feature map from the input image;
- perform a convolution operation on the feature map and reshape the feature map to produce a label map;
- divide the label map into equal subparts, which have the same height and width as the feature map;
- stack the subparts of the label map to produce a whole label map;
- and
- apply a convolution operation directly between the feature map and the whole label map without inserting extra values in deconvolutional layers to produce a semantic label map.
A method comprising:
- receiving an input image;
- producing a feature map from the input image;
- performing a convolution operation on the feature map and reshaping the feature map to produce a label map;
- dividing the label map into equal subparts, which have the same height and width as the feature map;
- stacking the subparts of the label map to produce a whole label map;
- and
- applying a convolution operation directly between the feature map and the whole label map without inserting extra values in deconvolutional layers to produce a semantic label map.
A non-transitory machine-useable storage medium embodying instructions which, when executed by a machine, cause the machine to:
- receive an input image;
- produce a feature map from the input image;
- perform a convolution operation on the feature map and reshape the feature map to produce a label map;
- divide the label map into equal subparts, which have the same height and width as the feature map;
- stack the subparts of the label map to produce a whole label map;
- and
- apply a convolution operation directly between the feature map and the whole label map without inserting extra values in deconvolutional layers to produce a semantic label map.
Dependencies are inferred from the claim text.