C2A: Human Detection in Disaster Scenarios (Combination to Application)

C2A: Combination to Application Dataset

Overview

This repository contains the code and information for the paper "UAV-Enhanced Combination to Application: Comprehensive Analysis and Benchmarking of a Human Detection Dataset for Disaster Scenarios" by Ragib Amin Nihal, Benjamin Yen, Katsutoshi Itoyama, and Kazuhiro Nakadai.

The C2A (Combination to Application) dataset is a novel synthetic dataset designed to advance human detection in disaster scenarios using UAV imagery. It combines disaster scene backgrounds from the AIDER dataset with human poses from the LSP/MPII-MPHB dataset to create a comprehensive resource for training machine learning models.

The full paper is available at: https://arxiv.org/pdf/2408.04922

Dataset

The C2A dataset consists of 10,215 images containing over 360,000 annotated human instances in various disaster scenarios. It includes diverse human poses (bent, kneeling, lying, sitting, upright) and disaster contexts (traffic incidents, fire, flood, collapsed buildings).

Usage Notes

• The dataset is split into training, validation, and test sets.
• Two annotation formats are provided for flexibility: YOLO and COCO.
• Pose information is available in a separate folder for all images.
• Users can choose between standard object detection (YOLO/COCO) or pose-aware detection (All labels with Pose info).

Key Features

• Synthetic dataset combining real disaster scenes with human poses
• Over 360,000 annotated human instances
• 5 human pose categories
• 4 disaster scenario types
• Image resolutions ranging from 123x152 to 5184x3456 pixels
• Designed to improve human detection in complex disaster environments