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.

翻译：图像采集条件与环境会显著影响计算机视觉中的高层任务，当在无畸变数据集上训练时，大多数计算机视觉算法的性能将受到限制。即使传感器等硬件和深度学习方法不断更新，在应对真实应用中的多变条件时仍会失效。本文应用目标检测器YOLOv7对CDCOCO数据集中的畸变图像进行检测。通过精心设计的优化策略，包括数据增强、检测框集成、去噪器集成、超分辨率模型以及迁移学习，所提模型在CDCOCO测试集上取得了优异性能。该去噪检测模型能够对畸变图像进行去噪与修复，使其可适用于多种真实场景与环境。