Detecting unfocused raindrops: In-vehicle multipurpose cameras
Abstract
Advanced driver assistance systems (ADASs) based on video cameras are becoming pervasive in today's automotive industry. However, while most of these systems perform nicely in clear weather conditions, their performances fail drastically in adverse weather and particularly in the rain. We present two novel approaches that aim to detect unfocused raindrops on a car windshield using only images from an in-vehicle camera. Based on the photometric properties of raindrops, the algorithms rely on image processing techniques to highlight them. The results will be used to improve ADAS behavior under rainy conditions. Both approaches are compared with each other and the techniques from the literature.
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Cord and Gimonet proposed an adherent raindrop detection algorithm that uses either background subtraction or a watershed approach.
“Cord and Gimonet [38], proposed an adherent raindrop detection algorithm that uses either background subtraction or a watershed approach for identifying raindrop candidates.”
From A Review of Detection and Removal of Raindrops in Automotive Vision Systems (2021) · cites this paper as [38]
CDR values were similar for the background subtraction and watershed approaches.
“Performance-wise, CDR values were similar for both approaches, but FPPI and dice showed better performance of watershed compared to background subtraction.”
From A Review of Detection and Removal of Raindrops in Automotive Vision Systems (2021) · cites this paper as [38]
In raindrop localization, the watershed-based approach was twice as slow as the background subtraction approach.
“In terms of raindrop localization, the watershed-based approach was twice as slow as the background subtraction one.”
From A Review of Detection and Removal of Raindrops in Automotive Vision Systems (2021) · cites this paper as [38]
FPPI stayed high, which was attributed to insufficient temporal filtering of 3 consecutive frames.
“In terms of raindrop prediction, FFPI (a measure of false detection) stayed high, which was attributed to insufficient temporal filtering (3 consecutive frames).”
From A Review of Detection and Removal of Raindrops in Automotive Vision Systems (2021) · cites this paper as [38]
Processing speed was the main drawback of Cord and Gimonet's algorithm.
“Processing speed was the main drawback of this algorithm.”
From A Review of Detection and Removal of Raindrops in Automotive Vision Systems (2021) · cites this paper as [38]
FPPI and dice showed better performance for watershed compared to background subtraction.
“Performance-wise, CDR values were similar for both approaches, but FPPI and dice showed better performance of watershed compared to background subtraction.”
From A Review of Detection and Removal of Raindrops in Automotive Vision Systems (2021) · cites this paper as [38]
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Zang, S., Ding, M., Smith, D., Tyler, P., Rakotoarivelo, T., & Kaafar, M. A. (2019) · IEEE Vehicular Technology Magazine · doi:10.1109/mvt.2019.2892497 · Cited by 457 · see citing works
A Review of Detection and Removal of Raindrops in Automotive Vision Systems
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“Cord and Gimonet [38], proposed an adherent raindrop detection algorithm that uses either background subtraction or a watershed approach for identifying raindrop candidates.”
MethodInfluential
- Authors
- Cord, A., & Gimonet, N.
- Venue
- IEEE Robotics & Automation Magazine
- Published
- 2014-03
- Volume
- 21
- Issue
- 1
- Pages
- 49-56
- ISSN
- 1070-9932 (ISSN-L)
- OpenAlex
- W2056111818
- Cited by
- 35
- References
- 19
- Citations by year
- 2025: 2 · 2024: 4 · 2023: 6 · 2022: 8 · 2021: 2 · 2020: 6 · 2019: 2 · 2018: 3 · 2017: 1 · 2016: 1
- FWCI
- 0.82
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- IEEE Robotics & Automation Magazine (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, in-vehicle camera, driver assistance systems, adverse weather, windshields, image processing
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