In this paper, we propose a unified end-to-end trainable multi-task network
that jointly handles lane and road marking detection and recognition that is
guided by a vanishing point under adverse weather conditions. We tackle rainy
and low illumination conditions, which have not been extensively studied until
now due to clear challenges. For example, images taken under rainy days are
subject to low illumination, while wet roads cause light reflection and distort
the appearance of lane and road markings. At night, color distortion occurs
under limited illumination. As a result, no benchmark dataset exists and only a
few developed algorithms work under poor weather conditions. To address this
shortcoming, we build up a lane and road marking benchmark which consists of
about 20,000 images with 17 lane and road marking classes under four different
scenarios: no rain, rain, heavy rain, and night. We train and evaluate several
versions of the proposed multi-task network and validate the importance of each
task. The resulting approach, VPGNet, can detect and classify lanes and road
markings, and predict a vanishing point with a single forward pass.
Experimental results show that our approach achieves high accuracy and
robustness under various conditions in real-time (20 fps). The benchmark and
the VPGNet model will be publicly available.

该论文提出了一个名为 VPGNet 的多任务神经网络模型，可以同时处理车道和路标检测和识别，在恶劣的天气条件下实现高效准确的检测，通过在消失点上的指引，能够在雨天、低照度和夜间等情况下成功地应对色彩畸变、光照反射等多种问题。