TY - JOUR
T1 - Real-time raindrop detection based on cellular neural networks for ADAS
AU - Al Machot, Fadi
AU - Ali, Mouhannad
AU - Haj Mosa, Ahmad
AU - Schwarzlmüller, Christopher
AU - Gutmann, Markus
AU - Kyamakya, Kyandoghere
PY - 2019
Y1 - 2019
N2 - A core aspect of advanced driver assistance systems (ADAS) is to support the driver with information about the current environmental situation of the vehicle. Bad weather conditions such as rain might occlude regions of the windshield or a camera lens and therefore affect the visual perception. Hence, the automated detection of raindrops has a significant importance for video-based ADAS. The detection of raindrops is highly time critical since video pre-processing stages are required to improve the image quality and to provide their results in real-time. This paper presents an approach for real-time raindrops detection which is based on cellular neural networks (CNN) and support vector machines (SVM). The major idea is to prove the possibility of transforming the support vectors into CNN templates. The advantage of CNN is its ultra fast precessing on embedded platforms such as FPGAs and GPUs. The proposed approach is capable to detect raindrops that might negatively affect the vision of the driver. Different classification features were extracted to evaluate and compare the performance between the proposed approach and other approaches.
AB - A core aspect of advanced driver assistance systems (ADAS) is to support the driver with information about the current environmental situation of the vehicle. Bad weather conditions such as rain might occlude regions of the windshield or a camera lens and therefore affect the visual perception. Hence, the automated detection of raindrops has a significant importance for video-based ADAS. The detection of raindrops is highly time critical since video pre-processing stages are required to improve the image quality and to provide their results in real-time. This paper presents an approach for real-time raindrops detection which is based on cellular neural networks (CNN) and support vector machines (SVM). The major idea is to prove the possibility of transforming the support vectors into CNN templates. The advantage of CNN is its ultra fast precessing on embedded platforms such as FPGAs and GPUs. The proposed approach is capable to detect raindrops that might negatively affect the vision of the driver. Different classification features were extracted to evaluate and compare the performance between the proposed approach and other approaches.
U2 - 10.1007/s11554-016-0569-z
DO - 10.1007/s11554-016-0569-z
M3 - Artikel
SN - 1861-8219
VL - 16
SP - 931
EP - 943
JO - Journal of Real-Time Image Processing
JF - Journal of Real-Time Image Processing
IS - 4
ER -