CD-COCO: A Versatile Complex Distorted COCO Database for Scene-Context-Aware Computer Vision
This provides a versatile database for computer vision researchers to enhance model robustness against realistic distortions, though it is incremental as it builds on existing MS-COCO data.
The authors tackled the problem of deep learning models being sensitive to image distortions by creating a new database, CD-COCO, which adds realistic local and global distortions to MS-COCO images based on scene context, depth, and semantics, resulting in improved robustness for tasks like object detection and scene segmentation.
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}}