  Though current object detection models based on deep learning have achieved
excellent results on many conventional benchmark datasets, their performance
will dramatically decline on real-world images taken under extreme conditions.
Existing methods either used image augmentation based on traditional image
processing algorithms or applied customized and scene-limited image adaptation
technologies for robust modeling. This study thus proposes a stylization
data-driven neural-image-adaptive YOLO (SDNIA-YOLO), which improves the model's
robustness by enhancing image quality adaptively and learning valuable
information related to extreme weather conditions from images synthesized by
neural style transfer (NST). Experiments show that the developed SDNIA-YOLOv3
achieves significant<EMAIL_ADDRESS>improvements of at least 15% on the real-world
foggy (RTTS) and lowlight (ExDark) test sets compared with the baseline model.
Besides, the experiments also highlight the outstanding potential of
stylization data in simulating extreme weather conditions. The developed
SDNIA-YOLO remains excellent characteristics of the native YOLO to a great
extent, such as end-to-end one-stage, data-driven, and fast.
