Tang Minan, Zhao Zixin, Qiu Jiandong
College of Automation and Electrical Engineering, Lanzhou Jiaotong University, Lanzhou 730050, China.
College of Electrical and Mechanical Engineering, Lanzhou Jiaotong University, Lanzhou 730070, China.
Sensors (Basel). 2024 Mar 20;24(6):1966. doi: 10.3390/s24061966.
This study addresses the ongoing challenge for learning-based methods to achieve accurate object detection in foggy conditions. In response to the scarcity of foggy traffic image datasets, we propose a foggy weather simulation algorithm based on monocular depth estimation. The algorithm involves a multi-step process: a self-supervised monocular depth estimation network generates a relative depth map and then applies dense geometric constraints for scale recovery to derive an absolute depth map. Subsequently, the visibility of the simulated image is defined to generate a transmittance map. The dark channel map is then used to distinguish sky regions and estimate atmospheric light values. Finally, the atmospheric scattering model is used to generate fog simulation images under specified visibility conditions. Experimental results show that more than 90% of fog images have AuthESI values of less than 2, which indicates that their non-structural similarity (NSS) characteristics are very close to those of natural fog. The proposed fog simulation method is able to convert clear images in natural environments, providing a solution to the problem of lack of foggy image datasets and incomplete visibility data.
本研究解决了基于学习的方法在雾天条件下实现精确目标检测这一持续存在的挑战。针对雾天交通图像数据集的稀缺问题，我们提出了一种基于单目深度估计的雾天天气模拟算法。该算法包括多个步骤：一个自监督单目深度估计网络生成相对深度图，然后应用密集几何约束进行尺度恢复以得到绝对深度图。随后，定义模拟图像的能见度以生成透射率图。然后使用暗通道图来区分天空区域并估计大气光值。最后，利用大气散射模型在指定能见度条件下生成雾模拟图像。实验结果表明，超过90%的雾图像的AuthESI值小于2，这表明它们的非结构相似性（NSS）特征与自然雾非常接近。所提出的雾模拟方法能够转换自然环境中的清晰图像，为缺乏雾天图像数据集和能见度数据不完整的问题提供了解决方案。
