Empowering teams to build world-leading AI products.
Cars & automobiles in general play a crucial role in our daily lives and most people would not deny the fact that driverless cars are the future that is set to revolutionize how we commute.
According to Goldman Sachs, the next 10 years are crucial for the auto industry as it will undergo a major transformation: the cars themselves, the companies that build them, and the customers – all will look significantly different than what it was before.
Industry:
As per a recent report by IHS Markit, it is forecasted that roughly 33 million AVs will hit the road by 2040 contributing to 26 percent of new car sales.
According to a recent report by Allied Market Research, the global autonomous vehicle market is projected to reach $556.67 billion by 2026, registering a CAGR of 39.47% from 2019 to 2026.
Empowering emerging technologies to ride the next wave of Connected Vehicles. Shaip is a leading AI Data Platform, providing high-quality data collection and annotation that powers ML & AI applications across the automotive industry.
We offer large volumes of image datasets (person, vehicle, traffic signs, road lanes) to train autonomous vehicles in a variety of scenarios and situations. Our experts can collect relevant image datasets as per your project requirements.
Collect actionable training video datasets like vehicular movement, traffic signals, pedestrians, etc. to train autonomous vehicles ML models. Each dataset is tailored specifically to meet your specific use case.
We have one of the most advanced image/video annotation tools in the
market that makes image labeling precise and super-functional for
complex use cases such as autonomous driving where quality is of utmost importance. Images & Videos are categorized frame by frame into objects such as pedestrians, vehicles, roads, lamp posts, traffic signs, etc. to build high-quality training data.
We help you with diverse labeling techniques after carefully studying your automotive project scope. We have a dedicated workforce trained for such complex annotation, QA teams that ensure 95%+ tagging accuracy levels, and tools to automate quality checks. Depending on your machine learning project, we would work on one or a combination of these image annotation techniques:
We can label images or videos with 360-degree visibility, captured by high-resolution cameras, to build high-quality, ground truth datasets that power autonomous vehicles algorithm.
Our experts use the box annotation technique to map objects in a given image/video to build datasets thereby enabling ML models to identify & localize objects.
In this technique, annotators plot points on object's (like Edge of Road, Broken Lane, End of Lane) exact edges to be annotated, regardless of their shape
In this technique, every pixel in an image/video is annotated with information & separated into different segments you need your cv algorithm to recognize
Auto-detect instances of semantic objects of a certain class in digital images and videos, use cases could include face detection and pedestrian detection.
Build highly accurate driver monitoring system by annotating facial landmarks such as eyes, head, mouth, etc. with accuracy & relevant metadata for blink detection and gaze estimation.
Annotate pedestrians in various images with 2D bounding boxes, to build high-quality training data for pedestrian tracking
Semantic Segmentation of images/videos frame by frame which includes objects such as pedestrians, vehicles – (cars, bicycles, buses), roads, lamp posts for building high-quality training data for AI-based autonomous vehicle systems.
Annotate hrs of images/videos frames of urban and street environments including cars, pedestrians, lamp posts, etc. to facilitate object detection to build high-quality training data for developing CV models for autonomous vehicle.
Reduce road accidents caused by drivers falling asleep by gathering vital driver information from facial landmarks such as drowsiness, eye gaze, distraction, emotion, & more. These in-cabin images are accurately annotated and used for training ML models.
Enhance Voice recognition in car or car's voice assistant by enabling drivers to make phone calls, control music, place orders, book services, schedule appointments & more. We offer vernacular datasets in 50+ languages to train your Car Voice Assistant.
Managed workforce for complete control, reliability & productivity
A powerful platform that supports different types of annotations
Minimum 95% accuracy ensured for superior quality
Global projects across 60+ countries
Enterprise-grade SLAs
Best-in-class real-life driving data sets
Images of driver’s face with car setup in different poses and variations covering unique participants from multiple ethnicities
Images of Vehicle License Plates from different angles
Annotated images (along with metadata) of different car interiors from multiple brands
Images of outdoor environments of street-level in urban areas or on highways with frequent traffic
Dedicated and trained teams:
Highest process efficiency is assured with:
The patented platform offers benefits:
Dedicated and trained teams:
Highest process efficiency is assured with:
The patented platform offers benefits:
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