Adverse weather conditions degrade the quality of images used in vision-based advanced
driver assistance systems (ADAS) and autonomous driving algorithms. Adherent raindrops …

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 …

Autonomous vehicles use cameras as one of the primary sources of information about the
environment. Adverse weather conditions such as raindrops, snow, mud, and others, can …

The automotive domain is highly regulated, with many safety-critical aspects to consider.
This means that a great deal of testing is required to validate the performance of automotive …

Input images are the main source of information for vision‐based algorithms. The presence
of raindrops in input images degrades their quality and, consequently, reduces the quality of …

Adverse weather conditions, such as rain, impact the visual quality of images and
significantly impact the performance of vision systems for drone-based video surveillance …

Advanced driver assistance systems (ADASs) based on video cameras are becoming
pervasive in today's automotive industry. However, while most of these systems perform …

The process of object detection utilizing deep learning is one of the most important deep
learning applications and computer vision techniques, where one can learn image features …

Vision based advance driver assistance system (ADAS) completely rely on the images
captured by the vehicle bound camera. Vision based ADAS also referred as computer vision …

Dynamic climatic conditions like rain, affects the performance of vision algorithms which are
used for surveillance and analysis tasks. Removal of rain-drops is challenging for single …