The recent development of deep learning methods applied to vision has enabled
their increasing integration into real-world applications to perform complex
Computer Vision (CV) tasks. However, image acquisition conditions have a major
impact on the performance of high-level image processing. A possible solution
to overcome these limitations is to artificially augment the training databases
or to design deep learning models that are robust to signal distortions. We opt
here for the first solution by enriching the database with complex and
realistic distortions which were ignored until now in the existing databases.
To this end, we built a new versatile database derived from the well-known
MS-COCO database to which we applied local and global photo-realistic
distortions. These new local distortions are generated by considering the scene
context of the images that guarantees a high level of photo-realism.
Distortions are generated by exploiting the depth information of the objects in
the scene as well as their semantics. This guarantees a high level of
photo-realism and allows to explore real scenarios ignored in conventional
databases dedicated to various CV applications. Our versatile database offers
an efficient solution to improve the robustness of various CV tasks such as
Object Detection (OD), scene segmentation, and distortion-type classification
methods. The image database, scene classification index, and distortion
generation codes are publicly available
\footnote{https://github.com/Aymanbegh/CD-COCO}

利用深度学习方法，通过引入复杂且逼真的扭曲来增强计算机视觉任务的鲁棒性，构建了一个多功能数据库，可用于改善目标检测、场景分割和扭曲类型分类等各种计算机视觉任务的可靠性。