2nd Place Solution for Waymo Open Dataset Challenge -- 2D Object Detection
This work addresses the need for reliable vehicle and person detection in autonomous driving systems, but it is incremental as it builds on existing detection methods.
The authors tackled 2D object detection for autonomous driving by integrating two-stage and one-stage detectors with anchor-free methods and using an auto ensemble scheme, achieving 70.28 L2 mAP on the Waymo Open Dataset v1.2 and ranking 2nd in the challenge.
A practical autonomous driving system urges the need to reliably and accurately detect vehicles and persons. In this report, we introduce a state-of-the-art 2D object detection system for autonomous driving scenarios. Specifically, we integrate both popular two-stage detector and one-stage detector with anchor free fashion to yield a robust detection. Furthermore, we train multiple expert models and design a greedy version of the auto ensemble scheme that automatically merges detections from different models. Notably, our overall detection system achieves 70.28 L2 mAP on the Waymo Open Dataset v1.2, ranking the 2nd place in the 2D detection track of the Waymo Open Dataset Challenges.