Citation

Hamzeh Y, Rawashdeh SA. J. Imaging 2021; 7(3): e52.

Copyright

(Copyright © 2021, MDPI: Multidisciplinary Digital Publications Institute)

DOI

10.3390/jimaging7030052

PMID

unavailable

Abstract

Research on the effect of adverse weather conditions on the performance of vision-based algorithms for automotive tasks has had significant interest. It is generally accepted that adverse weather conditions reduce the quality of captured images and have a detrimental effect on the performance of algorithms that rely on these images. Rain is a common and significant source of image quality degradation. Adherent rain on a vehicle's windshield in the camera's field of view causes distortion that affects a wide range of essential automotive perception tasks, such as object recognition, traffic sign recognition, localization, mapping, and other advanced driver assist systems (ADAS) and self-driving features. As rain is a common occurrence and as these systems are safety-critical, algorithm reliability in the presence of rain and potential countermeasures must be well understood. This survey paper describes the main techniques for detecting and removing adherent raindrops from images that accumulate on the protective cover of cameras.

Language: en

Keywords

deep learning; de-raining; rain detection; rain streak; raindrop