With the development of deep neural network generative models in recent
years, significant progress has been made in the research of depth estimation
in lane scenes. However, current research achievements are mainly focused on
clear daytime scenarios. In complex rainy environments, the influence of rain
streaks and local fog effects often leads to erroneous increases in the overall
depth estimation values in images. Moreover, these natural factors can
introduce disturbances to the accurate prediction of depth boundaries in
images. In this paper, we investigate lane depth estimation in complex rainy
environments. Based on the concept of convolutional kernel prediction, we
propose a dual-layer pixel-wise convolutional kernel prediction network trained
on offline data. By predicting two sets of independent convolutional kernels
for the target image, we restore the depth information loss caused by complex
environmental factors and address the issue of rain streak artifacts generated
by a single convolutional kernel set. Furthermore, considering the lack of real
rainy lane data currently available, we introduce an image synthesis algorithm,
RCFLane, which comprehensively considers the darkening of the environment due
to rainfall and local fog effects. We create a synthetic dataset containing 820
experimental images, which we refer to as RainKITTI, on the commonly used depth
estimation dataset KITTI. Extensive experiments demonstrate that our proposed
depth estimation framework achieves favorable results in highly complex lane
rainy environments.

本文研究复杂的雨天环境下的车道深度估计，在深度神经网络生成模型的基础上，通过卷积核预测的概念，提出了一个经过离线训练的双层像素级卷积核预测网络。同时，考虑到当前缺乏真实的雨天车道数据，引入了一个图像合成算法，RCFLane，用于综合考虑降雨和局部雾效所引起的环境变暗。通过在常用深度估计数据集 KITTI 上创建包含 820 个实验图像的合成数据集 RainKITTI，广泛实验证明了我们提出的深度估计框架在高度复杂的车道雨天环境中取得了良好的结果。

KPNDepth: 复杂雨天环境下车道图像的深度估计

KPNDepth: Depth Estimation of Lane Images under Complex Rainy  Environment

Cellular-connected unmanned aerial vehicles (UAVs) have gained increasing
attention due to their potential to enhance conventional UAV capabilities by
leveraging existing cellular infrastructure for reliable communications between
UAVs and base stations. They have been used for various applications, including
weather forecasting and search and rescue operations. However, under extreme
weather conditions such as rainfall, it is challenging for the trajectory
design of cellular UAVs, due to weak coverage regions in the sky, limitations
of UAV flying time, and signal attenuation caused by raindrops. To this end,
this paper proposes a physics-based trajectory design approach for
cellular-connected UAVs in rainy environments. A physics-based electromagnetic
simulator is utilized to take into account detailed environment information and
the impact of rain on radio wave propagation. The trajectory optimization
problem is formulated to jointly consider UAV flying time and
signal-to-interference ratio, and is solved through a Markov decision process
using deep reinforcement learning algorithms based on multi-step learning and
double Q-learning. Optimal UAV trajectories are compared in examples with
homogeneous atmosphere medium and rain medium. Additionally, a thorough study
of varying weather conditions on trajectory design is provided, and the impact
of weight coefficients in the problem formulation is discussed. The proposed
approach has demonstrated great potential for UAV trajectory design under rainy
weather conditions.

在极端天气条件下，本研究提出了一种基于物理的轨迹设计方法，利用基于物理的电磁模拟器考虑详细的环境信息和降雨对无线电波传播的影响，并通过深度强化学习算法基于多步学习和双 Q-learning 解决联合考虑无人机飞行时间和信干比的轨迹优化问题。在均匀大气和雨背景下比较了最优无人机轨迹，还对不同天气条件对轨迹设计的影响进行了彻底研究，并讨论了问题制定中的权重系数的影响。该方法在雨天气条件下展示了优秀的无人机轨迹设计潜力。