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.
| Task | Dataset | Metric | Value | Model |
|---|---|---|---|---|
| Autonomous Vehicles | BDD100K val | Accuracy (%) | 87.8 | YOLOPv2 |
| Autonomous Vehicles | BDD100K val | IoU (%) | 27.25 | YOLOPv2 |
| Autonomous Vehicles | BDD100K val | Params (M) | 38.9 | YOLOPv2 |
| Drivable Area Detection | BDD100K val | Params (M) | 38.9 | YOLOPv2 |
| Drivable Area Detection | BDD100K val | mIoU | 93.2 | YOLOPv2 |
| Lane Detection | BDD100K val | Accuracy (%) | 87.8 | YOLOPv2 |
| Lane Detection | BDD100K val | IoU (%) | 27.25 | YOLOPv2 |
| Lane Detection | BDD100K val | Params (M) | 38.9 | YOLOPv2 |
| 2D Object Detection | BDD100K val | Params (M) | 38.9 | YOLOPv2 |
| 2D Object Detection | BDD100K val | mIoU | 93.2 | YOLOPv2 |