  Nowadays, deep learning techniques are widely used for lane detection, but
application in low-light conditions remains a challenge until this day.
Although multi-task learning and contextual-information-based methods have been
proposed to solve the problem, they either require additional manual
annotations or introduce extra inference overhead respectively. In this paper,
we propose a style-transfer-based data enhancement method, which uses
Generative Adversarial Networks (GANs) to generate images in low-light
conditions, that increases the environmental adaptability of the lane detector.
Our solution consists of three parts: the proposed SIM-CycleGAN, light
conditions style transfer and lane detection network. It does not require
additional manual annotations nor extra inference overhead. We validated our
methods on the lane detection benchmark CULane using ERFNet. Empirically, lane
detection model trained using our method demonstrated adaptability in low-light
conditions and robustness in complex scenarios. Our code for this paper will be
publicly available.
