Lane Detection in Low-light Conditions Using an Efficient Data
Enhancement : Light Conditions Style Transfer
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by
Tong Liu, Zhaowei Chen, Yi Yang, Zehao Wu, Haowei Li
2020
Abstract
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 computation 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 models: the proposed Better-CycleGAN, light
conditions style transfer network and lane detection network. It does not
require additional manual annotations nor extra inference computation. 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.
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