Fog detection system based on computer vision techniques

Bronte, Sebastián; Bergasa, Luis M.; Alcantarilla, Pablo F.

doi:10.1109/itsc.2009.5309842

Cited by 78 publications

(47 citation statements)

References 13 publications

(10 reference statements)

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“…Based on this system we recorded images with daytime fog situations on highways. Afterwards the single images were labeled in the categories 3 Excluded, No Fog, Low Fog, Fog and Dense Fog, as defined in section III. For the labeling we plotted in every image a horizontal line at a distance of 100 m and 300 m. The pixel coordinates of these lines were calculated by perspective projection from the known camera setup and by assuming a flat world, i.e.…”

Section: Discussionmentioning

confidence: 99%

“…A single function, like the lane detection, works at 15 fps. 3 The categoriy Excluded contains images with overexposure or dazzle.…”

Section: Discussionmentioning

confidence: 99%

“…Another approach to estimate the visibility range under foggy weather conditions was proposed by Bronte et al [3].…”

Section: Previous Workmentioning

confidence: 99%

“…In this paper we address the problem of fog detection. Previous camera based fog detection systems analyze therefore distinct objects in the image [7] or image regions like the road region [4], [7], [18] or the horizon [3], [11], i.e. the position where the sky touches the road.…”

Section: Introductionmentioning

confidence: 99%

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Image based fog detection in vehicles

Pavlić

Belzner

Rigoll

et al. 2012

2012 IEEE Intelligent Vehicles Symposium

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Abstract-Modern vehicles are equipped with many cameras and their use in many practical applications is extensive. Detecting the presence of fog from images of a camera mounted in vehicles is a very challenging task with the potential to be used in many practical applications. Approaches introduced until now analyze properties of local objects in the image like lane markings, traffic signs, back lights of vehicles in front or head lights of approaching vehicles. By contrast to all these related works we propose to use image descriptors and a classification procedure in order to distinguish images with fog present from those free of fog. These image descriptors are global and describe the entire image using Gabor filters at different frequencies, scales and orientations. Our experiments demonstrated hight potential of the proposed method for fog detection on daytime images.

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Section: Discussionmentioning

confidence: 99%

“…A single function, like the lane detection, works at 15 fps. 3 The categoriy Excluded contains images with overexposure or dazzle.…”

Section: Discussionmentioning

confidence: 99%

“…Another approach to estimate the visibility range under foggy weather conditions was proposed by Bronte et al [3].…”

Section: Previous Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Image based fog detection in vehicles

Pavlić

Belzner

Rigoll

et al. 2012

2012 IEEE Intelligent Vehicles Symposium

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show abstract

“…Fog is known for its effects on visibility but the visual effects of fog differ between night and day, which explains why camera-based detection methods also differ. On-board daytime fog detection has been pioneered in [5] and further tackled in [6]. Mitigation of daytime fog has been addressed by restoring the contrast of images plagued by fog [7].…”

Section: Introductionmentioning

confidence: 99%

Towards night fog detection through use of in-vehicle multipurpose cameras

Gallen¹,

Cord²,

Hautiére³

et al. 2011

2011 IEEE Intelligent Vehicles Symposium (IV)

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Abstract-Compared to daytime, a larger proportion of accident happens during nighttime. The altered visibility of the road scene for the drivers may partially explain this situation. The latter becomes worse in fog presence. In this paper, two camera-based methods are proposed to detect the presence of night fog in images grabbed by in-vehicle multipurpose cameras. They rely on the visual effects of night fog. A first approach can assess the presence of fog around the vehicle thanks to the detection of the backscattered veil induced by the vehicle ego lights. It aims at detecting fog when the vehicle is alone in absence of exterior public lighting. A second approach can assess the presence of fog thanks to the detection of halos around light sources in the vehicle environment. It aims at detecting fog in presence of road traffic or public lighting. Both methods are presented and illustrated with actual images of fog. Their complementarity makes it possible to envision a complete night fog detection system. There are numerous applications for such a system: automation or adaptation of vehicle lights, contextual speed computation and reliability improvement for camera-based systems.

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Model Adaptation with Synthetic and Real Data for Semantic Dense Foggy Scene Understanding

Sakaridis

Dai

Hecker

et al. 2018

Lecture Notes in Computer Science

179

153

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This work addresses the problem of semantic scene understanding under dense fog. Although considerable progress has been made in semantic scene understanding, it is mainly related to clear-weather scenes. Extending recognition methods to adverse weather conditions such as fog is crucial for outdoor applications. In this paper, we propose a novel method, named Curriculum Model Adaptation (CMAda), which gradually adapts a semantic segmentation model from light synthetic fog to dense real fog in multiple steps, using both synthetic and real foggy data. In addition, we present three other main stand-alone contributions: 1) a novel method to add synthetic fog to real, clear-weather scenes using semantic input; 2) a new fog density estimator; 3) the Foggy Zurich dataset comprising 3808 real foggy images, with pixel-level semantic annotations for 16 images with dense fog. Our experiments show that 1) our fog simulation slightly outperforms a state-of-the-art competing simulation with respect to the task of semantic foggy scene understanding (SFSU); 2) CMAda improves the performance of state-of-the-art models for SFSU significantly by leveraging unlabeled real foggy data. The datasets and code will be made publicly available.

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Fog detection system based on computer vision techniques

Cited by 78 publications

References 13 publications

Image based fog detection in vehicles

Image based fog detection in vehicles

Towards night fog detection through use of in-vehicle multipurpose cameras

Model Adaptation with Synthetic and Real Data for Semantic Dense Foggy Scene Understanding

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