Evolutionary Image Quality Monitoring for ADAS under Adverse Weather

This work presents a method for monitoring the quality of the data captured by the cameras of an Advanced Driver Assistance System or an Autonomous Driving vehicle. Adverse weather conditions such as rain, snow, fog, or extreme light conditions can diminish the quality of images. Identifying the areas of an image affected by loss of quality due to different phenomena offers valuable information for subsequent critical tasks like object or lane tracking. Using simple features derived from the power spectrum of small image patches and a search strategy based on an evolutionary algorithm, we are able to efficiently locate the affected regions in the image.