Automotive

IMAGE AND VIDEO ANNOTATION FOR AUTOMOTIVE INDUSTRY

Keymakr provides professional data annotation for autonomous vehicles.
Our experienced in-house annotation teams will ensure that your machine learning for self-driving cars project achieves its goals.

Our proprietary annotation platform features a full suite of annotation techniques that can be adapted for your specific needs. Our annotators are comfortable working with all types, and qualities of data. We can also collect data for you from legal, open source repositories, or we can create bespoke data with our in-house studio.

BOUNDING BOXES FOR OBJECT DETECTION

POLYGON FOR IRREGULAR SHAPES DETECTION

SEMANTIC SEGMENTATION FOR OBJECT CLASSIFICATION

POLYLINE ANNOTATION FOR
LANE DETECTION

professional data annotation For Autonomous Vehicles

ANNOTATION FOR IN-CABIN AI

Keymakr in-cabin applications in autonomous vehicles:


In-cabin behaviour monitoring

Emotion recognition

In-cabin object recognition

Driver's assistant

USE CASES

Driver Monitoring Systems analyze multiple factors including: road conditions, steering response, facial expression and gaze to determine if the driver is dozing off at the wheel.

Keymakr can annotate drivers’ faces and expressions up to a pixel-perfect level of detail.

Semantic segmentation of all objects in the car including people in order to detect forgotten items in the car.

Skeletal annotation and movement tracking of the driver and passenger.

In-cabin AI is made possible by careful annotation. Keymakr’s unique project management systems empower developers by delivering valuable annotated video data quickly and at an affordable price.

BEST PERFORMING AI STARTS WITH ACCURATELY
ANNOTATED DATA

We offer training visuals for self-driving cars, as well as custom image annotation solutions for autonomous vehicles and other AI-backed transportation systems.

A fully-functioning and safe autonomous vehicle must be competent in a wide range of machine learning processes before it can be trusted to drive on its own. From processing visual data in real time to safely coordinating with other vehicles via IoT, the need for AI is essential. Self-driving cars could not do any of this without a huge volume of different types of training data, created and tagged for specific purposes.

To guarantee accuracy for your computer vision project Keymakr utilises three layers of human quality verification, followed by a final automated quality check. By making use of multiple instances of quality verification it is possible to create mistake free, annotated training data for autonomous vehicle deep learning.

ARTIFICIAL INTELLIGENCE DRIVES INTELLIGENT CARS

The number of autonomous cars and algorithms being tested on the road increases yearly. Accurate perception of the driving environment requires enormous amounts of data to be captured and carefully annotated.

AI learns to recognize the surroundings, detecting vehicles and objects, roads, lanes, road signs, traffic lights and other potential real-time hazards. Deep neural network algorithms enable autonomous cars to drive better than human-driven cars, achieving safer and more effective transportation.

Overcoming data bias is critical for the success of AI in autonomous driving. Keymakr, can play a part in troubleshooting persistent bias problems by creating and labeling varied, bespoke datasets. Our experienced teams of annotators can take on the burden of image and video annotation so that your data accurately reflects nighttime driving, low visibility weather, or road conditions in different countries.

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