Papers › YOLOPv2: Better, Faster, Stronger for Panoptic Driving Perception

YOLOPv2: Better, Faster, Stronger for Panoptic Driving Perception

24 Aug 2022arXiv:2208.11434archive 2025-07-28

Cheng Han, Qichao Zhao, Shuyi Zhang, Yinzi Chen, Zhenlin Zhang, Jinwei Yuan

Over the last decade, multi-tasking learning approaches have achieved promising results in solving panoptic driving perception problems, providing both high-precision and high-efficiency performance. It has become a popular paradigm when designing networks for real-time practical autonomous driving system, where computation resources are limited. This paper proposed an effective and efficient multi-task learning network to simultaneously perform the task of traffic object detection, drivable road area segmentation and lane detection. Our model achieved the new state-of-the-art (SOTA) performance in terms of accuracy and speed on the challenging BDD100K dataset. Especially, the inference time is reduced by half compared to the previous SOTA model. Code will be released in the near future.

PaperPDFCode

In Syntology Open this paper in Syntology's Atlas, the map of the papers in Syntology's graph and their citations.

Code

CAIC-AD/YOLOPv2 officialmentioned on GitHubpytorch report

Repository list and official/mentioned flags are the archive's, frozen 2025-07-28. Reachability, where shown, is from one Syntology probe window (2026-09-16 to 2026-09-18); repositories not probed show nothing. GitHub stars are not tracked.

Code Syntology ran Syntology

Not run by Syntology. Nothing on this page verifies that the listed code works.

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 YOLOPv2 Params (M) 38.9 #1 of 10 Archive leaderboard report
Drivable Area Detection BDD100K val YOLOPv2 mIoU 93.2 #1 of 10 Archive leaderboard report
Lane Detection BDD100K val YOLOPv2 Accuracy (%) 87.8 #8 of 11 Archive leaderboard report
Lane Detection BDD100K val YOLOPv2 IoU (%) 27.25 #8 of 11 Archive leaderboard report
Lane Detection BDD100K val YOLOPv2 Params (M) 38.9 #8 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

SPEED

Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections