Automotive AI

Automotive annotation

Label your automotive data with an expert Human in the Loop in your workflow

Perception tasks are becoming more and more complex and require the use of advanced 2D and 3D annotation tools for both images and video. Our expert labeling teams can not only prepare the training data for your solutions but also perform validation in order to improve your models.

Autonomous vehicles

With the future of the automotive industry moving towards autonomous vehicles, high-quality datasets are vital in creating safe and accurate models. Bounding boxes, polygons, full semantic segmentation, and cuboids drawn on 2D images are all techniques that can be used here.

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LiDar and sensor fusion

By labeling LiDar point cloud data coupled with GPS and visual data, models can be trained for the perception of objects and events in three dimensions over time across different sensors. 3D cuboids and even voxel segmentation is used for these use cases.

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Plate detection

Automatic registration plate detection and OCR can be critical for traffic monitoring and law enforcement purposes. Additional numbers such as USDOT codes on trucks and lorries can be detected for the efficient management of warehouses and parkings.

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Traffic monitoring

Being able to count, classify, and record vehicles is very important for a variety of uses such as traffic optimization and city planning. We are able to record rare events on hours of footage (jaywalking, traffic violations) across multiple cameras, in addition to vehicle recognition and counting.

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Damage detection

By coupling RGB imagery with infrared channels, labeling can be performed to enhance standard detection models in a variety of settings, including night-time imagery, small object detection at sea or in complex settings, or better classification of cars based on their infrared profile.

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Delivery robots

With home delivery becoming as common as ever, delivery robots could save time and money. However, as they will be navigating sidewalks and streets, it is important to label datasets which are built exclusively for their height and point of view.

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In-cabin monitoring

Labeling can be performed on in-cabin footage in order to ensure the safety of the driver and those around him or her. This includes pose detection, gaze tracking, weariness and sleepiness detection, as well as passenger and driver action monitoring in order to prevent crime.

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How would you like your data annotated?

Types of annotation

Bounding Box

Interested in having a Human in the Loop label your automotive dataset?
Get in touch with our team at Humans in the Loop and a project manager will help you find the best solution to your computer vision needs!