Versatility
The tool should be capable of handling different types of data, including 2D and 3D images, videos and point clouds generated by LiDAR sensors.
Industry
Automotive
Keylabs: fueling automotive AI with accurate data annotation
Data annotation tools are essential for the development of self-driving cars, as they enable the creation of high-quality training data to teach AI systems to operate vehicles safely and efficiently. Keylabs data annotation platform facilitates the annotation of various types of data — images, videos, LiDAR and sensor data — which are crucial for the proper functioning of autonomous vehicles.
The data annotation process typically involves labeling 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.
In-cabin AI
Data annotation tools
Some common applications of in-cabin automotive AI include:
01 / Driver Monitoring Systems (DMS)
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.
02 / Occupant detection & 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.
03 / 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.
04 / AI lane recognition
Lane detection is a critical component of self-driving car technology, identifying and tracking lanes on a roadway. Accurate annotation helps models learn to recognize different types of lanes, road conditions and lighting situations, allowing the car to make appropriate adjustments to its driving behavior.
Platform requirements
Key features
There are several key features that a robust data annotation tool for self-driving cars should possess:
Precision
High-quality annotations are crucial for the accuracy of AI models. The tool should enable precise labeling of objects and features, minimizing the chances of misinterpretation.
Scalability
A data annotation tool should be scalable to handle large datasets efficiently, streamlining the annotation process and reducing the time required for model training.
Automation
AI-powered data annotation tools can leverage machine learning algorithms to automate parts of the annotation process, speeding up the workflow and increasing overall efficiency.
Collaboration
A good data annotation tool should facilitate collaboration among team members, enabling multiple annotators to work together on the same dataset for consistency and speed.
Quality control
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.
Customization
A flexible data annotation tool should allow for customization to meet the unique needs of each project and adapt to new challenges in the industry.
Integration
The tool should integrate seamlessly with various machine learning frameworks and platforms, making it easier for developers to use annotated data for training.
Data security
A data annotation tool must prioritize data security and privacy, ensuring that the information is protected at all stages of the annotation process.
Applications
Use cases
Autonomous driving
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.
Box & line
In-cabin AI
Elevating in-vehicle experiences, our annotations bring cabin intelligence to life. We bridge the gap between AI and passengers, ensuring comfort, safety and responsiveness.
Multi-type
Recognition 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.
Box & polygon
Lane recognition
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.
Box & line
Privacy first
Data security
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 protection as a priority by utilizing a diverse array of security measures and industry best practices.
Learn more
Platform
Top features
Keylabs is created as a platform that incorporates state-of-the-art, performance oriented tools and processes.
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.
3D tool
Keylabs is a super-fast tool, soaring through Lidar files at ultra speeds. It seamlessly handles all file formats, ensuring a consistent, efficient workflow regardless of file complexity.
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.
Magic wand
Speeds up the annotation process by automatically detecting closed shapes of the same color or color gradient in a highly precise manner.
Object interpolation
Object interpolation accelerates annotation across a sequence of frames. Annotators label shape in the first and last keyframes, and the algorithm automatically generates labels for intermediate frames.
A-Z order
Objects can be placed on different leveled layers, which allows operators to correctly detect and work with those objects and their boundaries.
Multilayer annotation
Multilayer annotation allows multiple diverse annotations on a single piece of data such as an image or video frame, enriching the dataset with multiple facets of detail.
Object linking
Connects different instances of the same object across multiple frames or images, ensuring continuity and consistency of the annotation.
Hierarchical attributes
Using attribute hierarchies, it is possible to define structures of metadata for each item in the dataset using dependent attributes.
Workflow and task distribution
Workflow includes custom stages of annotation, verification, and final delivery. Task distribution ensures the right work goes to the right team member.
Data management
Data management is about strategically handling and organizing data throughout its lifecycle, from collection to annotation to archival.
Attributes interpolation
Attributes metadata can be interpolated between frames, automatically estimating object properties across keyframes.
Toolkit
Annotation types
Keylabs gives developers access to a full suite of annotation techniques:
Bounding box
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.
Polygon
A closed plane figure made up of several line segments joined together, used to define irregular shapes in an image.
Points
The point annotation tool places dots on images or videos, ideal for highlighting details like facial features, expressions and body postures.
Lines & multilines
Annotate linear structures like lane markings, cables and roads using straight or multi-segment lines.
Skeletons
Define custom skeleton topologies with any number of joints and connections for pose estimation and motion tracking.
Cuboid
3D bounding box capturing depth, height and width — essential for autonomous driving and AR/VR applications.
3D point cloud
Annotate LiDAR and 3D scene data with spatially-referenced points for high-fidelity spatial understanding.
Semantic segmentation
Pixel-level classification where every pixel in the image is assigned to a class label.
Instance segmentation
Pixel-level classification that distinguishes between multiple instances of the same object class.
Bitmap
Pixel-wise raster mask annotation allowing for highly precise object boundaries and gaps.
Mesh
Interconnected vertices forming polygonal structures — ideal for 3D reconstructions and digital twins.
Custom
Combine any of the above annotation techniques to accomplish specific annotation goals for your project.
Compliance
Enterprise-grade. Built for trust.
Keylabs meets the highest standards of data security and privacy.
