This leading automotive supplier needed to improve the interpretive accuracy of its models when identifying traffic lights and vulnerable road users.

“We just weren’t getting the results we needed to make this project work. Engagements with other vendors were only complicating things.”

– Head of Computer Vision

 

Problem 

This leading automotive supplier’s key product was struggling to accurately detect and interpret traffic lights and pedestrians, a fundamental aspect of their autonomous vehicle technology’s navigation ability and safety.

Solution

To improve traffic light detection, iMerit experts began by addressing a complex array of classification requirements by leveraging Ango, iMerit’s proprietary annotation tool. iMerit identified 27 unique classification requirements including traffic bulb counts, traffic light size, and other characteristics that were unique to the region their model was being deployed. 

Results

After implementing the new data into their training datasets, traffic light and vulnerable road user detection improved. Compared to previous projects, iMerit displayed a 40% increase in annotation accuracy coupled with a 50% reduction in time-per-task

 

 

BOTTOM LINE IMPACT

40%

Increased Annotation Accuracy

50%

Time-per-task Improvement

27

Unique Classification Requirements Identified