# DMRVisNet: Deep Multi-head Regression Network for Pixel-wise Visibility Estimation Under Foggy Weather

arXiv: https://arxiv.org/abs/2112.04278 · HTML: https://mlsanity.com/paper/2112.04278/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 53 references that are in the map.
- First posted on arXiv 8 Dec 2021.

## Builds on (most cited first, 9 of 9)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [AirSim: High-Fidelity Visual and Physical Simulation for Autonomous Vehicles](https://mlsanity.com/paper/1705.05065/) (2017)
- [Semantic Foggy Scene Understanding with Synthetic Data](https://mlsanity.com/paper/1708.07819/) (2017)
- [DehazeNet: An End-to-End System for Single Image Haze Removal](https://mlsanity.com/paper/1601.07661/) (2016)
- [Self-supervised Depth Estimation Leveraging Global Perception and Geometric Smoothness Using On-board Videos](https://mlsanity.com/paper/2106.03505/) (2021)
- [Expressway visibility estimation based on image entropy and piecewise stationary time series analysis](https://mlsanity.com/paper/1804.04601/) (2018)

## Built on by (most cited first, 5 of 5)

- [Self-supervised Multi-task Learning Framework for Safety and Health-Oriented Connected Driving Environment Perception using Onboard Camera](https://mlsanity.com/paper/2306.11822/) (2023)
- [CCDepth: A Lightweight Self-supervised Depth Estimation Network with Enhanced Interpretability](https://mlsanity.com/paper/2409.19933/) (2024)
- [CVVLSNet: Vehicle Location and Speed Estimation Using Partial Connected Vehicle Trajectory Data](https://mlsanity.com/paper/2410.00132/) (2024)
- [Traffic Image Restoration under Adverse Weather via Frequency-Aware Mamba](https://mlsanity.com/paper/2512.03852/) (2025)
- [Heuristic Style Transfer for Real-Time, Efficient Weather Attribute Detection](https://mlsanity.com/paper/2604.13947/) (2026)

## Abstract (arXiv)

Scene perception is essential for driving decision-making and traffic safety. However, fog, as a kind of common weather, frequently appears in the real world, especially in the mountain areas, making it difficult to accurately observe the surrounding environments. Therefore, precisely estimating the visibility under foggy weather can significantly benefit traffic management and safety. To address this, most current methods use professional instruments outfitted at fixed locations on the roads to perform the visibility measurement; these methods are expensive and less flexible. In this paper, we propose an innovative end-to-end convolutional neural network framework to estimate the visibility leveraging Koschmieder's law exclusively using the image data. The proposed method estimates the visibility by integrating the physical model into the proposed framework, instead of directly predicting the visibility value via the convolutional neural work. Moreover, we estimate the visibility as a pixel-wise visibility map against those of previous visibility measurement methods which solely predict a single value for an entire image. Thus, the estimated result of our method is more informative, particularly in uneven fog scenarios, which can benefit to developing a more precise early warning system for foggy weather, thereby better protecting the intelligent transportation infrastructure systems and promoting its development. To validate the proposed framework, a virtual dataset, FACI, containing 3,000 foggy images in different concentrations, is collected using the AirSim platform. Detailed experiments show that the proposed method achieves performance competitive to those of state-of-the-art methods.