The Impact of Rain on Image Quality From Sensors on Connected and Autonomous Vehicles

Abstract

As automated vehicles progress toward increasing levels of autonomy, the need for thorough testing of such systems in all relevant environments increases. These safety-critical systems often rely on visible-spectrum cameras to perceive the environment. Therefore, these systems must perform reliably under a range of adverse weather conditions. This study investigates the impact of rain on the quality of images taken in an experimental setting designed to vary rain intensity in a controlled manner. This study analyzes the impact of rain using low-level metrics such as contrast and spatial frequency response. In addition, overall image quality was evaluated using a range of full-reference image quality metrics. The results show a 45% reduction in SNR at 40 m and 38 mm/h, a 70% maximum decrease in Weber contrast at 30 m and 38 mm/h, and a 42% increase in color error as a result of rain in the environment. Consequently, degradation in image quality is likely to affect subsequent downstream computer vision performance. The results of this study highlight the need for robust testing and optimization of camera systems.