LiDAR Boxes
Object detection in LIDAR with 98% quality consistency
Challenge
Although one of the more straightforward LiDAR data processing tasks, object boxing is still challenging. For applications like ADAS, the task requires extreme precision. Mislabeling or inaccuracies in object detection can lead to faulty interpretations and safety risks. Scaling LIDAR data annotation is another challenge, calling for a large workforce with specialized skills and advanced training. Further, while scaling is underway, labeling quality must also stay consistent. Faced with the daunting task of finding a team capable of meeting such stringent need our client turned to DDD.
DDD’s Solution
DDD performed extremely well and rapidly scaled the team from 20 to 1,000+ annotators in just a few months, providing comprehensive training on the client’s platform so our team could use LiDAR and camera images to make determinations. We leveraged an annotator / reviewer process to exceed 98% quality (F1 and IOU adherence). By expanding the team, we now process more than 10,000K scenes per year, showcasing our ability to scale without compromising annotation quality.
Mastering object boxing for enhanced object tracking
Impact
DDD’s proficiency in LiDAR object boxing enhanced the precision of our client’s object detection capabilities, making its autonomous driving systems safer and more reliable. Today, those systems can identify and respond
to a broader range of road objects, reducing the risk of accidents and improving autonomous vehicle safety.