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 behaviour under rainy conditions. Both approaches are compared with each other and the techniques characterize rainy conditions. Both approaches are based on the photometric properties of the raindrops: brightness and blur. They rely on advanced image processing tools, such as morphological transformations, watershed and Background subtraction method is used to produce a mask localizing raindrops on the windshield. Finally, the rain detection is handled by counting the number of detected raindrops.
- Segmentation using Multilevel Thresholding
- Segmentation using Otsu technique.
- This type of segmentation is not convenient for raindrop detection technique.
- Less accuracy.
- Segmentation using Background Subtraction.
- Segmentation using watershed algorithm.