Accelerating Object Detection in Autonomous Vehicles

Accelerating Object Detection in Autonomous Vehicles

The Challenge:

An emerging autonomous vehicle startup was building a perception system for its Level 4 autonomous driving fleet. The core challenge was the lack of high-quality, multi-class annotated video data. Their in-house team struggled with maintaining consistency across frames and managing annotations for overlapping objects, particularly in complex urban settings with pedestrians, cyclists, road signs, and dynamic lighting.

Our Solution:

LabelCo.AI deployed a dedicated team of trained annotators specializing in computer vision for autonomous systems. We:

• Developed a custom annotation guideline for their specific use case, incorporating class hierarchies, edge cases, and labeling standards.

• Used frame-by-frame bounding boxes and object tracking across thousands of videos.

• Provided 3D cuboid annotations for depth perception and improved spatial understanding.

• Implemented a QA pipeline with dual reviews and AI-assisted auto-flagging for inconsistent bounding.

Results:

• Delivered 1.5 million accurately labeled frames within 8 weeks

• Improved object detection model precision by 23%

• Reduced internal data labeling costs by over 40%

• Helped the client meet their go-to-market deadline for pilot testing