System and method for semantic segmentation using hybrid dilated convolution (HDC)
A system and method for semantic segmentation using hybrid dilated convolution (HDC) 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 producing multiple convolution layers; grouping the multiple convolution layers into a plurality of groups; applying different dilation rates for different convolution layers in a single group of the plurality of groups; and applying a same dilation rate setting across all groups of the plurality of groups.
1. 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 hybrid dilated convolution (HDC) operation, the HDC 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 produce multiple convolution layers;
group the multiple convolution layers into a plurality of groups;
apply different dilation rates for different convolution layers in a single group of the plurality of groups; and
apply a same dilation rate setting across all groups of the plurality of groups
wherein the HDC operation is used by an autonomous control subsystem to control a vehicle without a driver.
2. The system of claim 1 wherein the HDC operation is configured to assign increasing dilation rates to the multiple convolution layers.
3. The system of claim 1 wherein the first dilation rate and the second dilation rate do not have a common factor relationship.
4. The system of claim 1 wherein the HDC operation is configured operate within a fully convolutional network (FCN).
5. The system of claim 1 wherein the HDC operation is performed at an original resolution, thereby enabling pixel-level decoding.
6. A method comprising:
receiving an input image;
producing a feature map from the input image;
performing a convolution operation on the feature map and producing multiple convolution layers;
grouping the multiple convolution layers into a plurality of groups;
applying different dilation rates for different convolution layers in a single group of the plurality of groups; and
applying a same dilation rate setting across all groups of the plurality of groups
wherein the method is used by an autonomous control subsystem to control a vehicle without a driver.
7. The method of claim 6 including assigning increasing dilation rates to the multiple convolution layers.
8. The method of claim 6 wherein the first dilation rate and the second dilation rate do not have a common factor relationship.
9. The method of claim 6 wherein the method operates within a fully convolutional network (FCN).
10. The method of claim 6 wherein the method is performed at an original resolution, thereby enabling pixel-level decoding.
11. 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 produce multiple convolution layers; group the multiple convolution layers into a plurality of groups;
apply different dilation rates for different convolution layers in a single group of the plurality of groups; and
apply a same dilation rate setting across all groups of the plurality of groups
wherein the instructions are further configured to be used with an autonomous control subsystem to control a vehicle without a driver.
12. The non-transitory machine-useable storage medium of claim 11 wherein the instructions are further configured to assign increasing dilation rates to the multiple convolution layers.
13. The non-transitory machine-useable storage medium of claim 11 wherein the first dilation rate and the second dilation rate do not have a common factor relationship.
14. The non-transitory machine-useable storage medium of claim 11 wherein the instructions are further configured to operate within a fully convolutional network (FCN).
15. The non-transitory machine-useable storage medium of claim 11 wherein the instructions are further configured to perform at an original resolution, thereby enabling pixel-level decoding.