Local Fog Detection Based on Saturation and RGB-Correlation

Alami, Salma; Ezzine, Abdelhak; Fouad, Elhassouni

doi:10.1109/cgiv.2016.10

Cited by 13 publications

(6 citation statements)

References 21 publications

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“…In [ 80 ], Alami et al considered only two parameters for fog detection: saturation and correlation. The selection of these parameters is based on the fog characteristics, color attenuation, and an increase in the white color.…”

Section: Fog Detection and Visibility Estimation Methodsmentioning

confidence: 99%

Visibility Enhancement and Fog Detection: Solutions Presented in Recent Scientific Papers with Potential for Application to Mobile Systems

Miclea

Ungureanu

Sandru

et al. 2021

Sensors

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In mobile systems, fog, rain, snow, haze, and sun glare are natural phenomena that can be very dangerous for drivers. In addition to the visibility problem, the driver must face also the choice of speed while driving. The main effects of fog are a decrease in contrast and a fade of color. Rain and snow cause also high perturbation for the driver while glare caused by the sun or by other traffic participants can be very dangerous even for a short period. In the field of autonomous vehicles, visibility is of the utmost importance. To solve this problem, different researchers have approached and offered varied solutions and methods. It is useful to focus on what has been presented in the scientific literature over the past ten years relative to these concerns. This synthesis and technological evolution in the field of sensors, in the field of communications, in data processing, can be the basis of new possibilities for approaching the problems. This paper summarizes the methods and systems found and considered relevant, which estimate or even improve visibility in adverse weather conditions. Searching in the scientific literature, in the last few years, for the preoccupations of the researchers for avoiding the problems of the mobile systems caused by the environmental factors, we found that the fog phenomenon is the most dangerous. Our focus is on the fog phenomenon, and here, we present published research about methods based on image processing, optical power measurement, systems of sensors, etc.

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Section: Fog Detection and Visibility Estimation Methodsmentioning

confidence: 99%

Visibility Enhancement and Fog Detection: Solutions Presented in Recent Scientific Papers with Potential for Application to Mobile Systems

Miclea

Ungureanu

Sandru

et al. 2021

Sensors

View full text Add to dashboard Cite

show abstract

“…Bronte et al detected fog by using a property that fog weakens the edge strength [15]. Alami et al used the correlation between saturation and RGB color channel in the HSV color model [16]. Pavlic et al trained features based on the power spectrum of the image through SVM [8].…”

Section: Related Workmentioning

confidence: 99%

“…In order to mitigate or solve the problems, fog detection may be considered as a pre-processing prior to fog removal step. Recently, various fog detection algorithms have been proposed [8]- [16]. Conventional fog detection algorithms require camera environment information or require prior learning.…”

Section: Introductionmentioning

confidence: 99%

Fast Fog Detection for De-Fogging of Road Driving Images

Jeong

Choi

Kim

et al. 2018

IEICE Trans. Inf. & Syst.

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SUMMARYAdvanced driver assistance system (ADAS) can recognize traffic signals, vehicles, pedestrians, and so on all over the vehicle. However, because the ADAS is based on images taken in an outdoor environment, it is susceptible to ambient weather such as fog. So, preprocessing such as de-fog and de-hazing techniques is required to prevent degradation of object recognition performance due to decreased visibility. But, if such a fog removal technique is applied in an environment where there is little or no fog, the visual quality may be deteriorated due to excessive contrast improvement. And in foggy road environments, typical fog removal algorithms suffer from color distortion. In this paper, we propose a temporal filter-based fog detection algorithm to selectively apply de-fogging method only in the presence of fog. We also propose a method to avoid color distortion by detecting the sky region and applying different methods to the sky region and the non-sky region. Experimental results show that in the actual images, the proposed algorithm shows an average of more than 97% fog detection accuracy, and improves subjective image quality of existing de-fogging algorithms. In addition, the proposed algorithm shows very fast computation time of less than 0.1ms per frame.

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“…It aims to judge whether sea fog is in the imagery, and mark the sea fog area at the pixel level. Most of the traditional sea fog detection algorithms are based on threshold segmentation methods [3]- [6]. In [7] and [8], they calculated the spectral characteristics of sea This paper was produced by the IEEE Publication Technology Group.…”

Section: Introductionmentioning

confidence: 99%