Keylabs: fueling automotive AI with accurate data annotation services.
Data annotation tools are essential for the development of self-driving cars, as they enable the creation of high-quality training data to teach artificial intelligence (AI) systems to operate vehicles safely and efficiently. Keylabs data annotation platform facilitates the annotation of various types of data, such as images, videos, LiDAR and sensor data, which are crucial for the proper functioning of autonomous vehicles.
The data annotation process for self-driving cars typically involves the process of labeling various elements within the collected data, such as vehicles, pedestrians, traffic signs and road markings. This annotated data is then used to train machine learning models that power the AI systems in self-driving cars. By providing accurate and diverse labeled data, Keylabs data annotation platform helps these models learn to recognize and interpret real-world scenarios, enabling the car to make appropriate decisions while navigating complex road environments.
These systems use AI to detect signs of driver fatigue, distraction, or impaired driving. Data annotation tools are employed to label relevant facial expressions, eye movements and body postures that indicate such behaviors. This enables the AI system to alert the driver or initiate preventive measures to ensure safety.
Occupant detection and comfort
In-cabin AI systems can detect and identify occupants in the vehicle, enabling personalized comfort settings and infotainment preferences. Data annotation tools help label features such as seating positions, body sizes and gestures, allowing the AI system to provide an optimized experience for each passenger.
In-cabin safety features
AI-powered safety features, such as child and pet detection, can help prevent accidents or incidents inside the vehicle. Data annotation tools are employed to label images and sensor data related to occupant positions, seatbelt usage and other safety-related factors, ensuring that the AI system can detect potential risks and issue appropriate warnings or take preventive actions.
AI lane recognition
Lane detection or lane keeping, is also a critical component of self-driving car technology. It refers to the process of identifying and tracking the lanes on a roadway using artificial intelligence (AI) algorithms. This capability is essential for maintaining safe driving conditions, ensuring that autonomous vehicles stay within their designated lanes and enabling smooth navigation in complex traffic scenarios.
Data annotation tools are used to label various elements within the collected data, such as lane markings, road boundaries and other important features. This annotated data is then used to train AI models responsible for lane recognition in self-driving cars. Accurate and diverse labeled data helps these models learn to recognize and interpret different types of lanes, road conditions and lighting situations, allowing the car to make appropriate adjustments to its driving behavior.
To ensure the highest level of accuracy, the tool should have built-in quality control features that allow for easy review and verification of annotated data. This helps maintain data integrity and improves the overall performance of the AI models being trained.
Paving the way for self-driving innovations, our data labeling solutions empower vehicles to navigate and decide with precision. Trust in a future where cars understand the road as intuitively as the driver.
Elevating in-vehicle experiences, our annotations bring cabin intelligence to life. We bridge the gap between AI and passengers, ensuring comfort, safety and responsiveness.
Recognittion of surroundings
Every tree, pedestrian and obstacle matters. Through meticulous data labeling, we teach AI systems to perceive and interpret the world around them with human-like acuity.
Stay centered and safe with lane recognition backed by our top-notch data annotation. Guiding vehicles seamlessly, our expertise ensures unbiased datasets for lane detection systems.
Using the Keylabs annotation tools comes with a commitment to data safety. Keylabs employs a range of security measures to protect valuable and sensitive data. This includes comprehensive infrastructure security if you choose to access Keylabs through the cloud. Alternatively, Keylabs can be installed on premises, guaranteeing you total control over access. We will continue to emphasize data protections as a priority by utilizing a diverse array of security measures and industry best practices.
ML assisted data annotation
ML assisted data annotation
Keylabs is a streamlined data labeling platform with AI-enhanced annotation.
Tailored for easy Integration with any client model and time & cost efficiency.
Keylabs’ advanced algorithms provide quick, accurate data prep for superior model training.
High performance video annotation
High performance video annotation
With the Keylabs platform's technical and software capabilities, video annotation is highly accurate (precision of up to 99,9% depending on project needs) and fast. Thanks to the geolocation adaptation of servers, even big-sized videos are loaded and processed quickly.
Object interpolation in the data annotation process is used to accelerate the annotation of objects across a sequence of frames in video annotation.
Annotators label the shape of an object in the first and the last keyframe of desired sequence and the object interpolation algorithm automatically generates the labels for the object in the intermediate frames.
It saves time and also ensures consistent labeling across frames.
Multilayer annotation is a complex yet valuable process in data annotation where different types of materials are layered onto a single item.
This allows the addition of multiple, diverse annotations to a single piece of data such as an image or video frame.
Each layer might provide a different dimension of information, enriching the dataset with multiple facets of detail.
This allows the addition of multiple, diverse annotations.
Object linking in the data annotation process is a valuable function that connects different instances of the same object across multiple frames or images.
For example, in video annotation, an object appearing in different frames is linked throughout the video, ensuring the continuity and consistency of the annotation.
The attribute is a type of tag that can be applied to a class or property to provide metadata about it.
Using attribute hierarchies, it is possible to define structures of metadata for each item in dataset.
It is achieved by using dependent attributes, which allows logical forming of metadata information for frame or object individually.
Workflow and task distribution
Workflow and task distribution
Workflow includes custom stages of one of 4 project stage types: annotation, verification, miscellanious and final.
Good workflow and task distribution ensure that the data annotation process is smooth, efficient and completed within the required timeframe.
Data management in the context of the data annotation process is about strategically handling and organizing the data throughout its lifecycle.
Effective data management helps to uphold data integrity and ensure that the final annotated data is accurate, consistent and ready for use in AI and machine learning projects.
Attributes metadata can also be interpolatable (changed) between the frames. For instance, In a self-driving car video annotation, this can label a car as a "sedan" going "30 mph" in the first frame & automatically estimate its type & speed in subsequent frames until the next key frame. This eliminates the need for manual annotation in each intervening frame, saving time & effort.
A rectangular box defined by coordinates that encapsulates an object of interest within an image
Oriented bounding box
A rotated rectangle that tightly encloses an object, accommodating its orientation and shape more precisely than a standard bounding box
A closed plane figure made up of several line segments that are joined together, used to define irregular shapes in an image
The Point Annotation Tool places dots on images or videos, ideal for highlighting details like facial features, expressions and body postures
Lines & Multilines
A data annotation tool used to draw single or multiple interconnected lines on images, capturing linear features or paths
A thin version of a shape, representing its central structure and providing a simplified representation of its form, commonly used in understanding object morphology or structure
The process of classifying and delineating each individual object instance in an image
The classification of each pixel in an image based on its semantic category, without distinguishing between individual object instances
A binary representation where each pixel value indicates whether it belongs to the object (1) or the background (0)
A 3D rectangular prism annotation, often used to represent objects in spatial dimensions
A collection of vertices, edges and faces that define the shape of a 3D object in space, often used in 3D modeling and computer graphics
3D Point Cloud
A collection of data points in a three-dimensional coordinate system, representing the external surface of an object
A tailored data annotation tool designed to cater to specific annotation needs not covered by standard tools