Abstract
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{\url{this https URL}}
Abstract (translated)
近年来，将深度学习方法应用于计算机视觉领域，使得它们越来越多地融入现实世界的应用中执行复杂的计算机视觉（CV）任务。然而，图像获取条件对高级图像处理任务的性能有很大的影响。克服这些限制的解决方案之一是人为增加训练数据集，或者设计具有对信号畸变鲁棒性的深度学习模型。我们在本文中选择第一个解决方案，通过向现有的数据库中添加复杂且真实的畸变，来丰富数据库。为了实现这一目标，我们基于著名的MS-COCO数据库构建了一个新的多用途数据库，并对其应用了局部和全局照片现实畸变。这些新的局部畸变是在考虑图片场景的上下文，从而保证高水平的照片现实畸变的基础上产生的。畸变通过利用场景中物体的深度信息和语义来生成。这保证了一个高水平的照片现实畸变，并使您能够探索在传统计算机视觉应用数据库中未被探索的现实场景。我们的多用途数据库为改善各种CV任务的稳健性提供了一种有效的解决方案，比如物体检测（OD）、场景分割和畸变类型分类方法。图像数据库、场景分类索引和畸变生成代码都是公开可用的 \footnote{\url{这个https:// URL}}
URL
https://arxiv.org/abs/2311.06976
