Development of most of the Computer Vision projects involves inferring knowledge from an image. Semantic Segmentation plays a key role in image analysis and helps a machine to recognize objects in an image. It is one of the high-level tasks that help to understand the complete scene in images.

What is Semantic Segmentation?

Semantic segmentation is the task of associating every pixel of an image with a corresponding class label. It is a type of pixel-level image classification which not just marks an image with labels and bounding box parameters but also gives a high-resolution image as the output.

Applications of Semantic Segmentation

Semantic segmentation has been successfully applied to tasks that involve segmentation of images. Below are a few key applications of Semantic Segmentation:

Autonomous Vehicles

Semantic Segmentation helps in equipping self-driving cars with the needed perception to understand the constantly changing environments and plan & execute the tasks with utmost precision.

It helps the autonomous vehicles to know exactly where other vehicles are and the free spaces on roads in addition to traffic signals and lane markings to safely navigate through the roads.

Medical Imaging

In the field of medical imaging, Semantic Segmentation helps to identify pixels of lesions or organs from CT or MRI images which in turn helps to understand the shapes and volumes of the organs. This aids the radiologists to perform better analysis and run diagnostic tests without delay.

Geo-sensing

Multi-class semantic segmentation helps to monitor land areas for deforestation and urbanization. It helps to recognize the type of land cover at the pixel level in satellite images. It also aids in city planning, road monitoring, and traffic management through detection of roads and buildings.

Precision Agriculture

Semantic Segmentation can help farming robots by assisting them in triggering weeding actions in real-time. It helps the robots to identify the right time for spraying out herbicides in the fields thereby reducing the need for manual monitoring.

Semantic Segmentation thus plays a crucial role in understanding images through the classification of each pixel in images. Pixel-level classification helps to achieve detection and delineation of objects thus enabling computers to perform accurate image analysis.