mlsanityData to 14 Oct 2026

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

arXiv 2108.04230 ↗First posted 27 Jul 2021cs.CV cs.AIcited by 6

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Abstract (arXiv)

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

Key facts. Cited by 6 papers in the map (data through 14 Oct 2026). Builds on 8 of its 9 references that are in the map. First posted on arXiv 27 Jul 2021.

Builds on

8 of 9 references.

References · 8

  1. 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,305
  2. 2014Microsoft COCO: Common Objects in Contextcited by 17,608
  3. 2019nuScenes: A multimodal dataset for autonomous drivingcited by 4,523
  4. 2016The Cityscapes Dataset for Semantic Urban Scene Understandingcited by 4,444
  5. 2020YOLOv4: Optimal Speed and Accuracy of Object Detectioncited by 1,630
  6. 2018BDD100K: A Diverse Driving Dataset for Heterogeneous Multitask Learningcited by 1,288
  7. 2021YOLOX: Exceeding YOLO Series in 2021cited by 1,071
  8. 2021OTA: Optimal Transport Assignment for Object Detectioncited by 178

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Cited by year

Citing papers per year: 2022: 2; 2023: 3; 2024: 1.

6 papers.