Image acquisition conditions and environments can significantly affect
high-level tasks in computer vision, and the performance of most computer
vision algorithms will be limited when trained on distortion-free datasets.
Even with updates in hardware such as sensors and deep learning methods, it
will still not work in the face of variable conditions in real-world
applications. In this paper, we apply the object detector YOLOv7 to detect
distorted images from the dataset CDCOCO. Through carefully designed
optimizations including data enhancement, detection box ensemble, denoiser
ensemble, super-resolution models, and transfer learning, our model achieves
excellent performance on the CDCOCO test set. Our denoising detection model can
denoise and repair distorted images, making the model useful in a variety of
real-world scenarios and environments.

通过优化图像增强、检测框集成、去噪集成、超分辨率模型和迁移学习等方法，将 YOLOv7 目标检测器应用于 CDCOCO 数据集，实现了在不同环境中对失真图像的准确检测和修复。该去噪检测模型在各种真实场景中具有广泛的应用价值。