论文标题
使用有效的数据增强的弱光条件下的车道检测：光条件样式转移
Lane Detection in Low-light Conditions Using an Efficient Data Enhancement : Light Conditions Style Transfer
论文作者
论文摘要
如今，深度学习技术已被广泛用于车道检测，但是在弱光条件下的应用仍然是一个挑战。尽管已提出了多任务学习和基于上下文的信息来解决该问题，但它们要么需要其他手动注释，要么分别引入额外的推理开销。在本文中，我们提出了一种基于样式转移的数据增强方法，该方法使用生成的对抗网络（GAN）在弱光条件下生成图像，从而增加了车道检测器的环境适应性。我们的解决方案由三个部分组成：拟议的SIM-Cyclegan，光条件样式转移和车道检测网络。它不需要其他手动注释或额外的推理开销。我们使用ERFNET验证了在车道检测基准Culane上的方法。从经验上讲，使用我们的方法训练的车道检测模型在复杂场景中表现出适应性和鲁棒性的适应性。我们的本文代码将公开可用。
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.
