Everything You Should Know About Data Annotation Services
What is Computer Vision
computer vision is a branch in artificial intelligence (AI) which allows computers and systems to extract useful information from videos, digital images and other inputs from the visual sphere -taking actions or offer recommendations based upon the information. If AI allows computers to consider, computers' computer vision can help them to look, feel, and be able to comprehend. Like a human being can simply look at an object to be able to comprehend the nature of it and what it is like it is possible for a computer to perform the same, provided that it is properly trained to be able to do it. Humans benefit from having lifetimes of context, which allows them to learn to distinguish objects and how far apart they are, if they're moving, and whether there is anything wrong with an image. Computer vision also performs the same tasks, however, it does so using cameras and data.
Computer vision systems have to be trained using annotated dataset, which allows the system to identify objects in the world. However there are a variety of kinds and methods that can be used to provide Data Annotation Services that may be required in accordance with the requirements for the particular project. In the next part, we will look at some of the more commonly used kinds of data annotation used in computer vision.
Image Annotation for Computer Vision
An image annotation is an additional type of annotation that focuses on still images. This allows images-specific data to be transmitted into the model of computer vision that is being developed. In certain instances the process of annotation for images could be automated, which can help in the progress of the project. However, there are instances where the annotation process has to be handled manually however, it will result in annotations that are better. This is an important aspect of the AI project since this is what allows computer vision to comprehend and interpret what it sees in the real world. After we have an overall overview of annotation on images we can look at the different types of annotations for images.
Types of Image Data Annotation for Computer Vision
Classification of images - This is giving a label or class to the entire image
Object recognition/defection - This involves identifying objects in images and videos
Boundary detection - In this case, the distinct boundaries are identified and localized between the objects in the scene
Semantic Segmentation - is a class-specific label for each one of the pixels within the photo is given
Segmentation of instances - Objects with instances are recognized and their boundaries are drawn
Panoptic segmentation - A task for image segmentation that combines the results of both instance and semantic segmentation to produce one general output
Video Annotation for Computer Vision
An annotation video is like Image Annotation in that it is primarily concerned with moving images. The annotations are applied to the objects of videos, and train an AI system to perceive the world in the same way as humans do. Data annotators are accountable for noting every frame of the video. This can be laborious since the video could be recorded in thirty frames per second (fps) or even 60 fps if the video is a quality video. Thus, even if have a film that's only two minutes long however, if it was recorded at 60 frames per second, you have 7,200 frames that must be noted.
Types of Video Data Annotation for Computer Vision
3D Cuboid annotation - can be used to provide precise 3D visualization of objects. It is a 3D bounding box technique helps identify the object's length, width, and depth while moving
Polylines - This type of annotation is mainly used in the automotive industry. It aids in the training of computers-based AI tools to identify street lanes in order to create high-precision autonomous vehicle systems.
Polygons Annotation - for polygons permits all of the object's edges to be marked regardless of its shape.
Event Tracking - This is the process of noting footage tracks with annotations, locating and labelling the important events in the time. This technique is utilized to identify every event relevant to.
Industries Where Computer Vision is Used Most Often
Automotive - Computer vision is an integral component in ADAS as well as autonomous cars. For instance the technology could be used to identify objects in the vicinity, identify the presence of pavement imperfections and obstacles, to enhance the driver's perception about the roads, improving his safety and making it possible for autonomous vehicles to travel the roads.
Agriculture - Computer vision is extensively used to check the state of the field as well as detect weeds, assess the condition of crops and track its growth. Farmers utilize computer vision in real-time to control livestock, spot abnormal behavior, and assess the animal health.
Manufacturing as well as Robotics - The technology permits industrial robots to "see" their surroundings and complete different tasks in factories. Computer vision can also be employed to increase safety of the workplace. For example you can track whether all employees wear helmets or maintain an appropriate distance from hazardous objects.
Construction - Businesses can utilize computers with computer vision to identify and categorize areas of construction to improve security and safety at sites of construction. Computer vision is currently employed for monitoring construction sites to detect potentially dangerous conditions, in addition, the speed of construction.
Computer Vision Use Case: Streamlining Car Damage Detection
A different project that we were able to think ahead in included the development of an AI solution that was able to not only recognize damage to vehicles but also provide customers with additional services including filing a claim, assessing the damages, predicting the type of repair needed and many other important details. We annotated 36,000 photos using 2-D bounding box. Certain images contained a variety of objects that needed annotation of various kinds, and annotation experts needed to be extra cautious when it came to recognizing objects and classifying them.
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