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.