Papers › YOLOPv2: Better, Faster, Stronger for Panoptic Driving Perception
YOLOPv2: Better, Faster, Stronger for Panoptic Driving Perception
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.
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Code
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| 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
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