arXiv 2311.069762023-11-12cs.CVcs.DB

CD-COCO：面向场景上下文感知计算机视觉的多功能复杂失真COCO数据库

CD-COCO: A Versatile Complex Distorted COCO Database for Scene-Context-Aware Computer Vision

IBISC Lab, Paris-Saclay University（巴黎萨克雷大学IBISC实验室）；L2TI Lab, Sorbonne Paris Nord University（索邦巴黎北大学L2TI实验室）；Norwegian University of Science and Technology (NTNU)（挪威科技大学）

Ayman Beghdadi, Azeddine Beghdadi, Malik Mallem, Lotfi Beji, Faouzi Alaya Cheikh

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AI总结：

针对现有数据库忽略复杂失真影响CV性能的问题，构建基于MS-COCO的CD-COCO数据库，利用场景上下文生成逼真局部与全局失真，提升目标检测等任务的鲁棒性。

摘要：

近年来，应用于视觉的深度学习方法的发展使其越来越多地集成到实际应用中，以执行复杂的计算机视觉（CV）任务。然而，图像采集条件对高级图像处理的性能有重大影响。克服这些限制的一种可能解决方案是人为扩充训练数据库，或设计对信号失真具有鲁棒性的深度学习模型。我们在此选择第一种方案，通过向数据库中添加现有数据库至今忽略的复杂且逼真的失真来丰富数据库。为此，我们构建了一个新的多功能数据库，该数据库源自著名的MS-COCO数据库，并对其应用了局部和全局的照片级逼真失真。这些新的局部失真通过考虑图像场景上下文生成，从而保证了高水平的照片逼真度。失真利用场景中物体的深度信息及其语义生成。这保证了高水平的照片逼真度，并允许探索传统数据库中未涵盖的真实场景，这些传统数据库专用于各种CV应用。我们的多功能数据库为提高各种CV任务（如目标检测（OD）、场景分割和失真类型分类方法）的鲁棒性提供了有效解决方案。图像数据库、场景分类索引和失真生成代码可公开获取。

英文摘要：

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{https://github.com/Aymanbegh/CD-COCO}}

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摘要：

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