C2A Dataset Computer Vision Project

For more details, please refer to our paper: Nihal, R. A., et al. "UAV-Enhanced Combination to Application: Comprehensive Analysis and Benchmarking of a Human Detection Dataset for Disaster Scenarios." ICPR 2024 (Accepted), arXiv preprint arXiv (2024).

and the github repo https://github.com/Ragib-Amin-Nihal/C2A

We encourage users to cite this paper when using the dataset for their research or applications.

The C2A (Combination to Application) Dataset is a resource designed to advance human detection in disaster scenarios using UAV imagery. This dataset addresses a critical gap in the field of computer vision and disaster response by providing a large-scale, diverse collection of synthetic images that combine real disaster scenes with human poses.

Context: In the wake of natural disasters and emergencies, rapid and accurate human detection is crucial for effective search and rescue operations. UAVs (Unmanned Aerial Vehicles) have emerged as powerful tools in these scenarios, but their effectiveness is limited by the lack of specialized datasets for training AI models. The C2A dataset aims to bridge this gap, enabling the development of more robust and accurate human detection systems for disaster response.

Sources: The C2A dataset is a synthetic combination of two primary sources:

1. Disaster Backgrounds: Sourced from the AIDER (Aerial Image Dataset for Emergency Response Applications) dataset, providing authentic disaster scene imagery.
2. Human Poses: Derived from the LSP/MPII-MPHB (Multiple Poses Human Body) dataset, offering a wide range of human body positions.

Key Features:

• 10,215 high-resolution images
• Over 360,000 annotated human instances
• 5 human pose categories: Bent, Kneeling, Lying, Sitting, and Upright
• 4 disaster scenario types: Fire/Smoke, Flood, Collapsed Building/Rubble, and Traffic Accidents
• Image resolutions ranging from 123x152 to 5184x3456 pixels
• Bounding box annotations for each human instance

Inspiration: This dataset was inspired by the pressing need to improve the capabilities of AI-assisted search and rescue operations. By providing a diverse and challenging set of images that closely mimic real-world disaster scenarios, we aim to:

1. Enhance the accuracy of human detection algorithms in complex environments
2. Improve the generalization of models across various disaster types and human poses
3. Accelerate the development of AI systems that can assist first responders and save lives

Applications: The C2A dataset is designed for researchers and practitioners in:

• Computer Vision and Machine Learning
• Disaster Response and Emergency Management
• UAV/Drone Technology
• Search and Rescue Operations
• Humanitarian Aid and Crisis Response

We hope this dataset will inspire innovative approaches to human detection in challenging environments and contribute to the development of technologies that can make a real difference in disaster response efforts.

                        @misc{
                            c2a-dataset_dataset,
                            title = { C2A Dataset Dataset },
                            type = { Open Source Dataset },
                            author = { saint tour },
                            howpublished = { \url{ https://universe.roboflow.com/saint-tour/c2a-dataset } },
                            url = { https://universe.roboflow.com/saint-tour/c2a-dataset },
                            journal = { Roboflow Universe },
                            publisher = { Roboflow },
                            year = { 2024 },
                            month = { aug },
                            note = { visited on 2024-11-24 },
                            }