Gain high-quality data and precision at scale to develop emerging technologies in the automotive industry. Netscribes automotive data annotation and labelling solution brings together advanced technology, top talent, and bespoke techniques to ensure a consistent supply of training data that is thoroughly validated and ready for deployment. Our always-on approach towards machine learning allows you to develop robust computer vision for self-driving cars, smart navigation systems, and other safe transport mechanisms, and confidently ride the next wave of connected vehicles.
Using bounding boxes and other related attributes we help your ML models understand and identify objects detected by vehicle sensors. Through this annotation type, our seasoned experts equip your systems to sense the object’s location, size, and orientation thus accurately labelling pedestrians, developing high-precision navigation systems that can guide autonomous vehicles along complex routes, and tracking the movement of other vehicles and objects on the road to avoid collisions.
Using key point annotation our experienced annotators deftly connect edges to help you accurately identify and classify different types of vehicles. Thus, you get robust training data to identify important landmarks like traffic lights, pedestrian crossings, stop signs, etc. It also helps train computer vision models to detect specific parts of a car, such as the wheels, doors, windows, and headlights, and build better ADAS systems.
Our professional annotators have expertise in polyline annotation which allows machine-learning models of automated vehicles to locate themselves within the large context of the road system. Using this technique we can empower your algorithms to detect lanes, improve aerodynamics and reduce drag in the vehicle design, and lower manufacturing defects, improving final product quality.
We use this technique to close an object within a polygon. By drawing a series of connected line segments around the area of interest, our experienced annotators help create training data for realistic virtual, testing, and simulating environments for autonomous driving technologies. This way we can support you in lowering costs and risks associated with physical testing.
Through semantic segmentation, our qualified annotators annotate complex images by separating different elements into multiple segments (or image objects) using unique colors and attributes creating and reviewing sophisticated data sets.
To generate precise datasets that effectively support algorithms used in autonomous vehicles, we label images and videos captured using high-resolution cameras with 360-degree visibility. With LiDAR data, we help you get essential intelligence to detect more objects and offer vehicles a deeper, detailed view of their surroundings to maximize roadway safety.
Our expert labelers and annotators use data from multi-sensor fusion to combine inputs from multiple RADARS, LiDARS, and cameras to form a single model or image of the environment around a vehicle. This way you get highly accurate results and a comprehensive understanding of the surrounding environment, weather conditions, road conditions, and other potential hazards. Using this annotation type, we help you develop advanced driver assistance systems (ADAS) and autonomous driving technologies that can adapt to changing environmental conditions.
Large dedicated team
100% cross-trained expert annotators
98% and above quality accuracy
Flexible Business Model
Platform & data-agnostic solutions
Our qualified annotators use advanced systems to quickly process thousands of data rows so your models get the information they need to work in the real world.
To ensure the highest quality data for machine learning applications, we have integrated validation, spot checks, and a worker’s seniority system into our quality assurance system.
A leading global engineering and technology company specializing in mobility solutions needed video and image annotation support for its recently established technology division to develop autonomous vehicles. They sought assistance in labelling objects by assigning codes for the autonomous driving application development.Read more