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

arXiv: https://arxiv.org/abs/2311.06976 · HTML: https://mlsanity.com/paper/2311.06976/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 22 references that are in the map.
- First posted on arXiv 12 Nov 2023.

## Builds on (most cited first, 10 of 10)

- [Towards Robust Monocular Depth Estimation: Mixing Datasets for Zero-shot Cross-dataset Transfer](https://mlsanity.com/paper/1907.01341/) (2019)
- [Object Detection in 20 Years: A Survey](https://mlsanity.com/paper/1905.05055/) (2019)
- [Object Detection with Deep Learning: A Review](https://mlsanity.com/paper/1807.05511/) (2018)
- [Benchmarking Robustness in Object Detection: Autonomous Driving when Winter is Coming](https://mlsanity.com/paper/1907.07484/) (2019)
- [Why do deep convolutional networks generalize so poorly to small image transformations?](https://mlsanity.com/paper/1805.12177/) (2018)
- [A Survey of Deep Learning-based Object Detection](https://mlsanity.com/paper/1907.09408/) (2019)
- [Robust and Accurate Object Detection via Adversarial Learning](https://mlsanity.com/paper/2103.13886/) (2021)
- [DeepCorrect: Correcting DNN models against Image Distortions](https://mlsanity.com/paper/1705.02406/) (2017)
- [Benchmarking performance of object detection under image distortions in an uncontrolled environment](https://mlsanity.com/paper/2210.15999/) (2022)
- [Residual Networks based Distortion Classification and Ranking for Laparoscopic Image Quality Assessment](https://mlsanity.com/paper/2106.06784/) (2021)

## Built on by (most cited first, 2 of 2)

- [Common Inpainted Objects In-N-Out of Context](https://mlsanity.com/paper/2506.00721/) (2025)
- [A New Lightweight Hybrid Graph Convolutional Neural Network -- CNN Scheme for Scene Classification using Object Detection Inference](https://mlsanity.com/paper/2407.14658/) (2024)

## Abstract (arXiv)

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 footnoteurlhttps://github.com/Aymanbegh/CD-COCO