Influence of Rain on Vision-Based Algorithms in the Automotive Domain

The Automotive domain is a highly regulated domain with stringent requirements that characterize automotive systems’ performance and safety. Automotive applications are required to operate under all driving conditions and meet high levels of safety standards. Vision-based systems in the automotive domain are accordingly required to operate at all weather conditions, favorable or adverse. Rain is one of the most common types of adverse weather conditions that reduce quality images used in vision-based algorithms. Rain can be observed in an image in two forms, falling rain streaks or adherent raindrops. Both forms corrupt the input images and degrade the performance of vision-based algorithms. This dissertation describes the work we did to study the effect of rain on the quality images and the target vision systems that use them as the main input. To study falling rain, we developed a framework for simulating failing rain streaks. We also developed a de-raining algorithm that detects and removes rain streaks from the images. We studied the relation between image degradation due to adherent raindrops and the performance of the target vision algorithm and provided quantitive metrics to describe such a relation. We developed an adherent raindrop simulator that generates synthetic rained images, by adding generated raindrops to rain-free images. We used this simulator to generate rained image datasets, which we used to train some vision algorithms and evaluate the feasibility of using transfer-learning to improve DNN-based vision algorithms to improve performance under rainy conditions.Ph.D.College of Engineering & Computer ScienceUniversity of Michigan-Dearbornhttp://deepblue.lib.umich.edu/bitstream/2027.42/170924/1/Yazan Hamzeh final dissertation.pdfDescription of Yazan Hamzeh final dissertation.pdf : Dissertatio