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Semantic Segmentation Services
//Image annotation
SEMANTIC SEGMENTATION
Semantic segmentation is a computer vision task that involves partitioning an image into multiple segments or regions and associating each segment with a semantic label that corresponds to a specific object category or class. Unlike object detection, which provides bounding boxes around objects, semantic segmentation assigns a class label to every pixel in the image, thus providing a pixel-level understanding of the image content.
//SEMANTIC SEGMENTATION
APPLICATIONS OF SEMANTIC SEGMENTATION
Pixel-Level Labeling
In semantic segmentation, each pixel in the input image is labeled with a corresponding class label, indicating the category or class to which it belongs. This enables fine-grained understanding and analysis of image content by providing detailed information about the spatial distribution and context of objects within the image.
Object Localization
Semantic segmentation enables precise object localization by delineating object boundaries at the pixel level. Instead of providing bounding boxes around objects, semantic segmentation accurately identifies and segments individual objects within the image, allowing for more precise object localization and boundary delineation.
Scene Understanding
Semantic segmentation provides a comprehensive understanding of the scene by segmenting the image into meaningful regions corresponding to different object categories or classes. This enables computers to comprehend the visual context of the scene and recognize objects, structures, and spatial relationships within the image.
Applications in Autonomous Driving
Semantic segmentation is widely used in autonomous driving systems for tasks such as road and lane detection, vehicle detection, pedestrian detection, and obstacle detection. By segmenting the scene into semantic regions, autonomous vehicles can understand and interpret the surrounding environment, enabling safe navigation and decision-making on the road.
//Industries
USE CASES FOR SEMANTIC SEGMENTATION
RETAIL
Assisting the retail and e-commerce sectors by providing training data to optimize their in-store operations through the implementation of artificial intelligence (AI).ROBOTICS
3D object detection finds extensive application in robotics, particularly to prevent collisions with dynamic entities such as humans, animals, and other objects.AGRICULTURE
Supporting agriculture through computer vision training data involves facilitating the identification of product defects, sorting produce, managing livestock, assessing soil quality, implementing fertilizer applications, and fine-tuning genetic conditions.INSURANCE
Preparing training data to integrate AI into insurance procedures for tasks such as risk assessment, fraud detection, underwriting and minimizing human error.HEALTHCARE
Incorporating annotations and accurate labeling within AI systems is crucial for uncovering connections within genetic codesand enhancing efficiency in healthcare processes.SECURITY & SURVEILLANCE
Facilitating the integration of AI into cameras and sensors enables the detection of potential risks at workplaces, airports, and industrial sites. This involves incorporating computer vision technology into security and surveillance systems.SELF-DRIVING
Bounding boxes serve to annotate the surroundings of a vehicle, aiding in the detection of various objects including pedestrians, vehicles, traffic signs, and barriers.LOGISTICS
Logistics represents one of the growing areas of artificial intelligence application. We specialize in annotating images of goods to generate high-quality training data utilized in logistics.AUTONOMOUS FLYING
Simplifying and broadening access to AI implementations for automated or assisted flight can be achieved by leveraging image annotation conducted at the backend using training data specifically tailored for autonomous flying.// Drop us a line! We are here to answer your queries