CCNet

Acquire images from car-mounted cameras.

Preprocess the images to the appropriate input size for CCNet.

Feed the images through the trained CCNet model.

Obtain the semantic segmentation map.

Use the segmentation map for path planning and obstacle avoidance.

Acquire medical images (MRI, CT scans).

Preprocess the images to the appropriate input size for CCNet.

Feed the images through the trained CCNet model.

Obtain the semantic segmentation map highlighting the anatomical structures.

Use the segmentation map for diagnosis and treatment planning.

Acquire satellite images.

Preprocess the images to the appropriate input size for CCNet.

Feed the images through the trained CCNet model.

Obtain the semantic segmentation map showing different land cover types.

Use the segmentation map for environmental monitoring and urban planning.

Capture images of manufactured products using a high-resolution camera.

Preprocess the images for CCNet input.

Run the images through the trained CCNet model.

Generate a segmentation map highlighting defective regions.

Automatically flag the defective products for removal from the production line.

Capture real-time images from robot-mounted cameras.

Preprocess the images for CCNet input.

Process images through the CCNet model for semantic segmentation.

The robot uses the segmentation maps to navigate, interact with objects, and perform tasks in its environment.