论文标题
YOLOPV2：更好，更快，更强大
YOLOPv2: Better, Faster, Stronger for Panoptic Driving Perception
论文作者
论文摘要
在过去的十年中，多任务学习方法在解决全景驱动感知问题方面取得了令人鼓舞的结果，提供了高精度和高效率的性能。在设计用于实时实时自动驾驶系统的网络时，它已成为一个流行的范式，在该系统中，计算资源受到限制。本文提出了一个有效，有效的多任务学习网络，以同时执行交通对象检测，可驱动的道路区域细分和车道检测的任务。我们的模型以挑战性的BDD100K数据集的准确性和速度来实现新的最新性能（SOTA）性能。特别是，与以前的SOTA模型相比，推理时间减少了一半。代码将在不久的将来发布。
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.
