Weather Recognition Based on Images Captured by Vision System in Vehicle
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Camera-based rain-sensing provides benefits over FIR-based ones, in terms of improved detection rate, glare reduction, and detection of other particles like dust or salt on the windshield.
“Camera-based rain-sensing provides benefits over FIR-based ones, in terms of improved detection rate, glare reduction, and detection of other particles like dust or salt on the windshield.”
From A Review of Detection and Removal of Raindrops in Automotive Vision Systems (2021) · cites this paper as [14]
Yan et al.'s algorithm was much faster than the other classifiers.
“In terms of speed, the algorithm was much faster than the others.”
From A Review of Detection and Removal of Raindrops in Automotive Vision Systems (2021) · cites this paper as [14]
Yan et al.'s HSV classifier performed better than KNN and AdaBoost.MH, but slightly lower than AdaBoost with one-vs-all.
“The algorithm performed better than KNN and AdaBoost.MH, but it was slightly lower than AdaBoost with one-vs-all.”
From A Review of Detection and Removal of Raindrops in Automotive Vision Systems (2021) · cites this paper as [14]
Yan et al.'s algorithm is good for classifying rainy conditions but less appropriate for vehicle ADAS applications.
“This algorithm is good for classifying rainy conditions but it is less appropriate for vehicle ADAS applications.”
From A Review of Detection and Removal of Raindrops in Automotive Vision Systems (2021) · cites this paper as [14]
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- Authors
- Yan, X., Luo, Y., & Zheng, X.
- Affiliations
- Tsinghua University (CN)
- Venue
- Lecture Notes in Computer Science
- Publisher
- Springer Berlin Heidelberg
- Published
- 2009
- Pages
- 390-398
- ISSN
- 0302-9743 (ISSN-L)
- 1611-3349
- OpenAlex
- W2162374190
- Cited by
- 92
- References
- 17
- Citations by year
- 2026: 3 · 2025: 2 · 2024: 7 · 2023: 8 · 2022: 11 · 2021: 12 · 2020: 7 · 2019: 10 · 2018: 8 · 2017: 9 · 2016: 6 · 2015: 3 · 2014: 3 · 2012: 2
- FWCI
- 1.43
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- Lecture notes in computer science (publishedVersion)
- Topics
- Physical Sciences › Engineering › Automotive Engineering › Autonomous Vehicle Technology and Safety
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Image Enhancement Techniques
- Keywords
- HSV color histogram, Real AdaBoost, weather classification, driver assistance
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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