Semantic Segmentation plays an important role in analyzing images and helps a machine to identify objects in images. It is a high-level task that enables machines to comprehend complete scenes in images. Most of the Computer Vision projects require inferring knowledge from images that can be achieved through Semantic Segmentation.
What is Semantic Segmentation?
Semantic Segmentation involves associating every pixel in an image with an appropriate class label. One can define it as a type of image classification at the pixel level that not just annotates an image with labels and bounding box parameters but also a good resolution image as the output.
Applications of Semantic Segmentation
Many companies have successfully applied it to tasks that involve image segmentation. The following are the applications of Semantic Segmentation:
Precision Agriculture
Semantic Segmentation helps to train farming robots on triggering actions related to weeding in real-time. It helps to reduce manual monitoring by enabling the robots to identify the right time for spraying herbicides in the fields.
Autonomous Vehicles
Semantic Segmentation equips the autonomous vehicles with the much-needed perception that enables them to understand the ever-changing environments and plan and implement the tasks with high precision. It empowers the self-driving cars to identify the free spaces and places occupied by other vehicles in addition to lane markings and traffic signals that enable them to safely drive through the roads.
Geo-sensing
Multi-class Semantic Segmentation tasks aids in the monitoring of land areas for urbanization and deforestation. In the satellite images, it helps in recognizing the type of land at the pixel level. It is also very useful in planning cities, monitoring roads, and managing traffic through the detection of buildings and roads.
Medical Imaging
In the medical imaging field, Semantic Segmentation helps to understand the volumes and shapes of organs by identifying pixels of lesions or organs from MRI or CT images. This enables the radiologists to perform efficient analysis and perform diagnostic tests without any delays.
This image classification toolset plays a key role in helping machines understand images by classifying every pixel in images. Pixel-level classification enables computers to perform image analysis tasks accurately by helping then achieve detection and delineation of objects.
About RightClick.AI
RigthClick.AI specializes in offering pixel-perfect segmentation services to help build your machine learning models with accuracy and precision. Drop a mail to info@rightclick.ai for more details.