Images of road scenes in low-light situations are lack of details which could increase crash risk of connected autonomous vehicles (CAVs). Therefore, an effective and efficient image enhancement model for low-light images is necessary for safe CAV driving. Though some efforts have been made, image enhancement still cannot be well addressed especially in extremely low light situations (e.g., in rural areas at night without street light). To address this problem, we developed a light enhancement net (LE-net) based on the convolutional neural network. Firstly, we proposed a generation pipeline to transform daytime images to low-light images, and then used them to construct image pairs for model development. Our proposed LE-net was then trained and validated on the generated low-light images. Finally, we examined the effectiveness of our LE-net in real night situations at various low-light levels. Results showed that our LE-net was superior to the compared models, both qualitatively and quantitatively.

在弱光条件下的道路场景图像缺乏细节，这可能会增加连接的自动驾驶汽车（CAV）的撞车风险。因此，对于安全的CAV驱动而言，用于低光图像的有效且高效的图像增强模型是必需的。尽管已经做出了一些努力，但是图像增强仍然不能很好地解决，特别是在光线非常暗的情况下（例如，在没有路灯的夜晚的农村地区）。为了解决这个问题，我们开发了基于卷积神经网络的光增强网（LE-net）。首先，我们提出了一种生成管线，将白天图像转换为低光图像，然后使用它们来构建用于模型开发的图像对。然后，我们对建议的LE-net进行了训练，并在生成的低光图像上进行了验证。最后，我们研究了LE-net在各种弱光条件下在真实夜晚情况下的有效性。结果表明，我们的LE-net在质量和数量上均优于比较模型。