Proceedings of the 2022 IEEE Conference on Intelligent Transportation Systems (ITSC 2022)
Abstract:
Impressive progress has recently been made in
deep learning based lane detection for the autonomous vehicle
domain using an in-car camera. However, relatively little attention was paid to lane detection under bad weather conditions.
The general difficulty stems from the water on the road or
raindrops remaining on the windscreen and hampering lane
detectability. In this paper, we propose a lane enhancement
approach to improve lane detection accuracy under rain. We
formulate image enhancement as an image-to-image translation
problem, and devise semi-supervised techniques to efficiently
learn from an image set containing images from source domain
(rain images) and target domain (clear images). Our semisupervision approach differs from the conventional unsupervised image-to-image translation, in that a small amount of
labelled rain images are added to the target domain in order
to guide the translation to focus on enhancing the lanes while
preserving the background. Specifically, we first compute the
road regions in an image using vanishing points from camera
intrinsic matrix. We then define a loss function using the
road regions as constrains, in order to enforce lane-aware
image generation. As a result, new rain images are generated
by highlighting the lanes explicitly in thick bright lines. Our
empirical results show that using only a few labelled images,
our proposed semi-supervised learning is able to enhance lanes
efficiently and improving lane detection significantly.
License type:
Publisher Copyright
Funding Info:
This research / project is supported by the Singapore government’s Research - Innovation and Enterprise 2020 plan (Advanced Manufacturing and Engineering domain) and administered by the Agency for Science, Technology and Research
Grant Reference no. : I2001E0063