Image generation for improving the robustness of computer vision models in autonomous driving
Abstract
Autonomous driving systems are poised to revolutionize transportation, enhancing safety, efficiency, and accessibility. However, adverse weather conditions, such as heavy rain, dense fog, snow, and nighttime darkness, remain critical challenges, often compromising the performance of object detection systems supporting the perception of vehicles. This thesis aims to address these challenges by developing novel techniques for enhancing the robustness of object detection systems under adverse weather conditions, using state-of-the-art deep learning models and innovative data augmentation and denoising strategies. The research leverages diffusion models, specifically the Instruct Pix2Pix variant of Stable Diffusion, to simulate realistic adverse weather conditions on existing images and with that perform a data augmentation and to develop a specialized denoising model to mitigate weather-induced distortions. Using both simulated data from the CARLA simulator and real-world datasets such as BDD100K and ACDC, this study evaluates the performance of widely used object detection models, including Faster R-CNN and YOLOv10, with the aim of first analyzing and investigating whether the developed techniques are effective in the simulated environment and subsequently validate them in real-world scenarios. Key contributions include the creation of augmented datasets incorporating synthetic adverse weather conditions and the development of a denoising model to improve image clarity. The…
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