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Determining 3D Flow Fields via Multi-camera Light Field Imaging

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A Foggy Weather Simulation Algorithm for Traffic Image Synthesis Based on Monocular Depth Estimation.

Minan Tang¹, Zixin Zhao², Jiandong Qiu²

¹College of Automation and Electrical Engineering, Lanzhou Jiaotong University, Lanzhou 730050, China.

Sensors (Basel, Switzerland)

|March 28, 2024

Summary

This summary is machine-generated.

Researchers developed a novel foggy weather simulation algorithm using monocular depth estimation. This method generates realistic fog images from clear ones, addressing the scarcity of foggy datasets for object detection research.

Keywords:

atmospheric scattering model fog simulation monocular depth estimation natural scene statistics traffic safety

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Area of Science:

Computer Vision
Image Processing
Artificial Intelligence

Background:

Object detection in foggy conditions is challenging for learning-based methods.
Existing foggy traffic image datasets are scarce, hindering research and development.

Purpose of the Study:

To propose a novel foggy weather simulation algorithm.
To address the lack of diverse foggy image datasets for training and evaluating object detection models.

Main Methods:

Utilized self-supervised monocular depth estimation for relative and absolute depth map generation.
Employed dense geometric constraints for scale recovery.
Defined visibility to generate transmittance maps.
Estimated atmospheric light values using dark channel maps.
Applied atmospheric scattering models for fog simulation.

Main Results:

Generated fog simulation images closely mimic natural fog characteristics (AuthESI < 2 for >90% of images).
The method successfully converts clear images into realistic foggy scenes.
Validated the effectiveness of the proposed fog simulation algorithm.

Conclusions:

The developed algorithm provides a viable solution for augmenting foggy image datasets.
Enables more robust training and evaluation of object detection systems in adverse weather.
Facilitates advancements in autonomous driving and intelligent transportation systems.