Bounding Boxes
Rare object detection in autonomous navigation
Challenge
For autonomous vehicles to navigate safely, models must recognize standard road features and rare objects—emergency vehicles, animals, roadblocks, and unusual pedestrian scenarios, such as a person in a wheelchair. These rare objects cause problems because they appear infrequently but complicate the road environment. Our client needed its dataset to include rare objects, but labeling them called for a sophisticated ontology and a team skilled in rare object annotation.
DDD’s Solution
We quickly scaled up a team of 50 annotators and led them through an intensive multi-week training program to familiarize them with an extensive ontology of hundreds of rare objects. Today, our team processes 50K+ images each month, of which approximately 20K are rare objects. Given that rare objects appear infrequently and their nature varies depending on geography and environment, we chose an inter-annotator agreement approach to ensure consistency and accuracy in labeling.
Excelling in complex ontology and precision annotation for enhanced vehicle safety.
Impact
The result is 99.5% quality in identifying scenes with rare objects and 98% label accuracy. The precision and reliability of our team’s spline annotation work allowed our client to complete a successful test in the first city and expand into several others. The expansion speaks to our client’s confidence in our ability to deliver reliable, accurate data. Our client’s cloud-driven mapping model seamlessly adapts and makes real-time adjustments based on road changes.