  Object detection models represented by YOLO series have been widely used and
have achieved great results on the high quality datasets, but not all the
working conditions are ideal. To settle down the problem of locating targets on
low quality datasets, the existing methods either train a new object detection
network, or need a large collection of low-quality datasets to train. However,
we propose a framework in this paper and apply it on the YOLO models called
DiffYOLO. Specifically, we extract feature maps from the denoising diffusion
probabilistic models to enhance the well-trained models, which allows us
fine-tune YOLO on high-quality datasets and test on low-quality datasets. The
results proved this framework can not only prove the performance on noisy
datasets, but also prove the detection results on high-quality test datasets.
We will supplement more experiments later (with various datasets and network
architectures).
