2nd Place Solution for Waymo Open Dataset Challenge -- 2D Object Detection
arXiv:2006.15507
Abstract
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.
References in corpus (8)
- Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
- MMDetection: Open MMLab Detection Toolbox and Benchmark
- Weighted boxes fusion: Ensembling boxes from different object detection models
- Deformable Convolutional Networks
- Aggregated Residual Transformations for Deep Neural Networks
- Scalability in Perception for Autonomous Driving: Waymo Open Dataset
- Hybrid Task Cascade for Instance Segmentation
- 1st Place Solutions for OpenImage2019 -- Object Detection and Instance Segmentation