基于卷积神经网络的雾天道路能见度测量方法

doi:10.12305/j.issn.1001-506X.2026.04.02

Abstract

Abstract:

In order to improve road visibility in foggy weather, enhance road safety and driving efficiency. This article proposes a visibility detection method based on atmospheric scattering model and convolutional neural network by constructing a visibility detection and restoration method based on road monitoring images, to achieve the detection of fog concentration. Furthermore, the foggy images are processed to remove fog and enhance the clarity of road monitoring images in foggy weather. By using automatic feature extraction and attention mechanism modules, a relationship model between foggy optical image features and visibility is established to improve the poor applicability of visibility detection methods in complex highway scenes; The strategy of using transmittance feature pixels and depth of field distance pixel feature extraction is adopted to optimize the estimation of image transmittance and target depth of field to improve the accuracy of road visibility detection. By using a synthetic dataset containing fog images for training and testing with actual foggy images captured by high-speed monitoring, fog concentration detection and evaluation under different conditions have been achieved, which can be used for highway warning and prompting in intelligent transportation systems.

Key words: visibility detection, convolutional neural network (CNN), optical image enhancement, atmospheric scattering model

CLC Number:

TP 391.41

Jingrong SUN, Hua ZHANG, Zhezhe CHEN, Fangzheng ZHAO. Method for measuring road visibility in foggy condition based on convolutional neural network[J]. Systems Engineering and Electronics, 2026, 48(4): 1112-1124.

Figures/Tables 19

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数据集	数量
KITTI	14999
SOTS	1000
REDE	810
FRIDA	330

真实等级	识别等级
真实等级	1	2	3	4	5	6
1	0.882	0.117	0.023	0.0032	0.002	0.001
2	0.089	0.861	0.055	0.0014	0.003	0.001
3	0.007	0.087	0.881	0.0091	0.004	0.002
4	0.005	0.023	0.054	0.8174	0.043	0.021
5	0.003	0.011	0.021	0.0093	0.823	0.063
6	0.001	0.003	0.032	0.0042	0.085	0.843

算法	FRIDA 数据集	KITTI 数据集	SOTS 数据集	高速数据集
VGG16	93.12	92.11	94.98	78.43
depth+trans	94.21	91.42	94.46	79.33
ResNet50	98.32	93.52	98.78	87.33
本文算法	99.12	96.12	99.46	89.43

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Method for measuring road visibility in foggy condition based on convolutional neural network

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