Systems and methods for instance segmentation based on semantic segmentation

A method for obtaining an instance segmentation or panoptic segmentation of an image based on semantic segmentation, on a computing device including a storage medium storing a set of instructions, and a processor in communication with the storage medium to execute the set of instructions, the method comprising:

• performing semantic segmentation on an input image to obtain a semantic label map having specific set of classes, using a trained semantic segmentation model;
• generating a boundary map, using a trained generator, based on the obtained semantic label map concatenated with the input image;
• and
• processing the boundary map, using a post-processing step, to differentiate objects of the specific set of classes to obtain the instance segmentation or panoptic segmentation of the input image, wherein the post-processing step comprises performing Breadth-First-Search for each enclosed area of the semantic label map to get a mask for each enclosed area, a class of the mask being determined based on its semantic label map.

A computer non-transitory readable medium, storing a set of instructions for obtaining an instance segmentation or panoptic segmentation of an image based on semantic segmentation, wherein when the set of instructions is executed by a processor of an electrical device, the electrical device performs a method comprising:

• performing semantic segmentation on an input image to obtain a semantic label map having specific set of classes, using a trained semantic segmentation model;
• generating a boundary map, using a trained generator, based on the obtained semantic label map concatenated with the input image;
• and
• processing the boundary map, using a post-processing step, to differentiate objects of the specific set of classes to obtain the instance segmentation or panoptic segmentation of the input image, wherein the instance segmentation or panoptic segmentation is obtained in real time, wherein the post-processing step comprises performing Breadth-First-Search for each enclosed area of the semantic label map to get a mask for each enclosed area, a class of the mask being determined based on its semantic label map.