Workshop on Autonomous Driving at CVPR 2021: Technical Report for Streaming Perception Challenge

Songyang Zhang; Lin Song; Songtao Liu; Zheng Ge; Zeming Li; Xuming He; Jian Sun

Workshop on Autonomous Driving at CVPR 2021: Technical Report for Streaming Perception Challenge

Computer Vision and Pattern Recognition 2021-08-10 v1 Artificial Intelligence

Authors: Songyang Zhang , Lin Song , Songtao Liu , Zheng Ge , Zeming Li , Xuming He , Jian Sun

Abstract

In this report, we introduce our real-time 2D object detection system for the realistic autonomous driving scenario. Our detector is built on a newly designed YOLO model, called YOLOX. On the Argoverse-HD dataset, our system achieves 41.0 streaming AP, which surpassed second place by 7.8/6.1 on detection-only track/fully track, respectively. Moreover, equipped with TensorRT, our model achieves the 30FPS inference speed with a high-resolution input size (e.g., 1440-2304). Code and models will be available at https://github.com/Megvii-BaseDetection/YOLOX

Keywords

autonomous driving object detection

Cite

@article{arxiv.2108.04230,
  title  = {Workshop on Autonomous Driving at CVPR 2021: Technical Report for Streaming Perception Challenge},
  author = {Songyang Zhang and Lin Song and Songtao Liu and Zheng Ge and Zeming Li and Xuming He and Jian Sun},
  journal= {arXiv preprint arXiv:2108.04230},
  year   = {2021}
}

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Report of the 1st Place of Streaming Perception Challenge(Workshop on Autonomous Driving at CVPR 2021)

Workshop on Autonomous Driving at CVPR 2021: Technical Report for Streaming Perception Challenge

Abstract

Keywords

Cite

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