YOLOP: You Only Look Once for Panoptic Driving Perception

By Dongsheng Wu, Man-Wen Liao, Wei-Tian Zhang, Xinggang Wang, Xiang Bai, Wenqing Cheng, Wen-Yu Liu. Machine Intelligence Research, 2021

462 citations recorded by the source corpus.

A panoptic driving perception system is an essential part of autonomous driving. A high-precision and real-time perception system can assist the vehicle in making reasonable decisions while driving. We present a panoptic driving perception network (you only look once for panoptic (YOLOP)) to perform traffic object detection, drivable area segmentation, and lane detection simultaneously. It is composed of one encoder for feature extraction and three decoders to handle the specific tasks. Our model performs extremely well on the challenging BDD100K dataset, achieving state-of-the-art on all three tasks in terms of accuracy and speed. Besides, we verify the effectiveness of our multi-task learning model for joint training via ablative studies. To our best knowledge, this is the first work that can process these three visual perception tasks simultaneously in real-time on an embedded device Jetson TX2(23 FPS), and maintain excellent accuracy. To facilitate further research, the source codes and pre-trained models are released at https://github.com/hustvl/YOLOP.

Subfields: Automotive Engineering, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/05e40e7209a3bb4233b3c15c19a3ab9f8c459919

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Automotive Engineering

  1. Yu Qiao
  2. Xiaosong Jia
  3. Peng Wu
  4. Li Chen
  5. Junchi Yan
  6. Hongyang Li
  7. Pravendra Singh
  8. Pranav Singh Chib

In Computer Vision and Pattern Recognition

  1. Junchi Yan
  2. Hongyang Li
  3. Li Chen
  4. Yu Qiao
  5. Xiaosong Jia
  6. Peng Wu
  7. Luc Van Gool
  8. Xiaogang Wang

Citations in the corpus