SS-SFR: Synthetic Scenes Spatial Frequency Response on Virtual KITTI and Degraded Automotive Simulations for Object Detection
<|reference_start|>SS-SFR: Synthetic Scenes Spatial Frequency Response on Virtual KITTI and Degraded Automotive Simulations for Object Detection: Automotive simulation can potentially compensate for a lack of training data in computer vision applications. However, there has been little to no image quality evaluation of automotive simulation and the impact of optical degradations on simulation is little explored. In this work, we investigate Virtual KITTI and the impact of applying variations of Gaussian blur on image sharpness. Furthermore, we consider object detection, a common computer vision application on three different state-of-the-art models, thus allowing us to characterize the relationship between object detection and sharpness. It was found that while image sharpness (MTF50) degrades from an average of 0.245cy/px to approximately 0.119cy/px; object detection performance stays largely robust within 0.58\%(Faster RCNN), 1.45\%(YOLOF) and 1.93\%(DETR) across all respective held-out test sets.<|reference_end|>
@article{jakab2024ss-sfr:,
    title={SS-SFR: Synthetic Scenes Spatial Frequency Response on Virtual KITTI and
  Degraded Automotive Simulations for Object Detection},
    author={Daniel Jakab, Alexander Braun, Cathaoir Agnew, Reenu Mohandas, Brian
  Michael Deegan, Dara Molloy, Enda Ward, Tony Scanlan, Ciar'an Eising},
    journal={Proceedings of the Irish Machine Vision and Image Processing
  Conference 2024},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.15646},
    primaryClass={cs.CV}
}