Raindrop detection on car windshields using geometric-photometric environment construction and intensity-based correlation
Abstract
Vision-based Driver Assistance Systems (DAS) are becoming pervasive in today's automotive industry. However, most of these systems are designed to perform in good weather conditions and they perform very poorly in adverse weather particularly in rain. A big problem related to rainy weather conditions that highly limits the performance of DAS is raindrops on car windshields. We present a novel approach that detects raindrops on a car windshield using only a single image from an in-vehicle camera and a standard interest point detector for pre-selection of raindrop candidates. The algorithm models the geometric shape of a raindrop on the car windshield, utilizes its photometric properties and establishes a relationship between raindrop and environment. The proposed algorithm outperforms existing machine vision-based approaches for the task of raindrop modeling and detection from an in-vehicle perspective. It functions very accurately and is robust in terms of imprecise positions of raindrop candidates. Its results can be further used for image restoration and vision enhancement and hence it is a valuable tool for DAS.
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Due to their irregular shapes, closeness to image sensors, and long temporal presence in captured image frames, adherent raindrop detection and removal problem is relatively harder than that of rain streaks detection and removal.
“Due to their irregular shapes, closeness to image sensors, and long temporal presence in captured image frames, adherent raindrop detection and removal problem is relatively harder than that of rain streaks detection and removal [11,12].”
From A Review of Detection and Removal of Raindrops in Automotive Vision Systems (2021) · cites this paper as [12]
Halimeh and Roser's RIGSEC model is cohesive and dynamic, and considers the effect of background variations on raindrop appearance at different areas of the captured image.
“The RIGSEC model is a cohesive, dynamic model, that considers the effect of background variations on raindrop appearance, at different areas of the captured image.”
From A Review of Detection and Removal of Raindrops in Automotive Vision Systems (2021) · cites this paper as [12]
The RIGSEC model is relatively complex and computationally expensive.
“It is, however, relatively complex and computationally expensive.”
From A Review of Detection and Removal of Raindrops in Automotive Vision Systems (2021) · cites this paper as [12]
Halimeh and Roser attributed the low CC results to the raindrop lens effect and blurriness of raindrop appearance due to camera focus near infinity.
“Halimeh and Roser attributed this to the raindrop lens effect and blurriness of raindrop appearance, due to camera focus near infinity.”
From A Review of Detection and Removal of Raindrops in Automotive Vision Systems (2021) · cites this paper as [12]
MaxCorr results were good, but CC results were low.
“Maxcorr results were also good but CC results were low.”
From A Review of Detection and Removal of Raindrops in Automotive Vision Systems (2021) · cites this paper as [12]
Results from artificial pattern and drive scene showed good visual similarity between real and modeled raindrops.
“Results of artificial pattern and drive scene showed good visual similarity between real and modeled raindrop.”
From A Review of Detection and Removal of Raindrops in Automotive Vision Systems (2021) · cites this paper as [12]
Further experiments showed that matching the constructed environment within a small ROI made raindrop positions converge to the optimum and CC values converge to MaxCorr values.
“Further experiments showed that matching the constructed environment within a small ROI, raindrop positions converge to the optimum, and CC values converge to MaxCorr values as well.”
From A Review of Detection and Removal of Raindrops in Automotive Vision Systems (2021) · cites this paper as [12]
Halimeh and Roser used the RIGSEC model to detect adherent raindrops on special tiltable planes, with artificial patterns and real traffic scenes.
“Halimeh and Roser [12] used the RIGSEC model to detect adherent raindrops on special tiltable planes, with artificial patterns and real traffic scenes.”
From A Review of Detection and Removal of Raindrops in Automotive Vision Systems (2021) · cites this paper as [12]
The lower raindrop detection performance was attributed to inaccuracies in initial localization of candidate raindrops using the SURF algorithm.
“In terms of raindrop detection, the lower performance was attributed to inaccuracies in the initial localization of candidate raindrops, using the SURF algorithm.”
From A Review of Detection and Removal of Raindrops in Automotive Vision Systems (2021) · cites this paper as [12]
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Video-based raindrop detection for improved image registration
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“Halimeh and Roser [12] used the RIGSEC model to detect adherent raindrops on special tiltable planes, with artificial patterns and real traffic scenes.”
BackgroundMethodResultInfluentialVideo-based water drop detection and removal method for a moving vehicle
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A novel technique for raindrop detection on a car windshield using geometric-photometric model
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“(Halimeh and Roser, 2009) proposed the method based on a geometric-photometric model that described the refractive property of a raindrop on a windshield.”
Method
- Authors
- Halimeh, J. C., & Roser, M.
- Affiliations
- Karlsruhe Institute of Technology (DE)
- ORCID
- Halimeh, Jad C.
- Venue
- 2009 IEEE Intelligent Vehicles Symposium
- Publisher
- IEEE
- Published
- 2009-06
- Pages
- 610-615
- ISSN
- 1931-0587 (ISSN-L)
- OpenAlex
- W2137031670
- Cited by
- 76
- References
- 16
- Citations by year
- 2026: 2 · 2025: 5 · 2024: 3 · 2023: 1 · 2022: 2 · 2021: 7 · 2020: 11 · 2019: 8 · 2018: 5 · 2017: 5 · 2016: 1 · 2015: 4 · 2014: 2 · 2013: 4 · 2012: 8
- FWCI
- 7.64
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- IEEE Intelligent Vehicles Symposium (publishedVersion)
- Topics
- Physical Sciences › Engineering › Automotive Engineering › Autonomous Vehicle Technology and Safety
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Image Enhancement Techniques
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Vision and Imaging
- Keywords
- raindrop detection, driver assistance systems, rainy weather, in-vehicle camera, geometric modeling, photometric properties, keypoint detection, image restoration, vision improvement
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