Papers › YOLOP: You Only Look Once for Panoptic Driving Perception

YOLOP: You Only Look Once for Panoptic Driving Perception

25 Aug 2021arXiv:2108.11250archive 2025-07-28

Dong Wu, Manwen Liao, Weitian Zhang, Xinggang Wang, Xiang Bai, Wenqing Cheng, Wenyu Liu

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 the reasonable decision while driving. We present a panoptic driving perception network (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.

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ap_per_class hustvl/yolop/lib/core/evaluate.py official repository unverified MIT (permissive) · 983459be55dc0e1c · report
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Tasks

Autonomous DrivingDrivable Area DetectionLane DetectionMulti-Task LearningObject DetectionTraffic Object Detectionobject-detection

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Drivable Area Detection BDD100K val YOLOP Params (M) 7.9 #5 of 10 Archive leaderboard report
Drivable Area Detection BDD100K val YOLOP mIoU 91.5 #5 of 10 Archive leaderboard report
Lane Detection BDD100K val YOLOP Accuracy (%) 70.5 #9 of 11 Archive leaderboard report
Lane Detection BDD100K val YOLOP IoU (%) 26.2 #9 of 11 Archive leaderboard report
Lane Detection BDD100K val YOLOP Params (M) 7.9 #9 of 11 Archive leaderboard report

Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.

Methods

Introduced by this paper: YOLOP

YOLOP

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