IDFire: Image Dataset for Indoor Fire Load Recognition

Accurate fire load (combustible objects) information is crucial for safety design and resilience assessment of buildings. Traditional fire load acquisition methods, such as fire load survey, which are time-consuming, tedious, and error-prone, failed to adapt to dynamic changed indoor scenes. As a starting point of automatic fire load estimation, fast recognition and detection of indoor fire load are important. Thus, A dataset containing images of indoor scenes and annotations of instance segmentation is developed in this research. In total, 1015 images are contained in the dataset, distributed across five typical scenes: bedroom, dining room, hospital, living room, and office. 

精准的火灾荷载（可燃物）信息对于建筑的安全设计与韧性评估至关重要。传统的火灾荷载获取方法（如火灾荷载普查）不仅耗时冗长、操作繁琐且易产生误差，难以适配动态变化的室内场景。作为自动火灾荷载估算的起点，快速识别与检测室内火灾荷载具有重要意义。因此，本研究构建了一套包含室内场景图像与实例分割标注的数据集。该数据集总计包含1015张图像，涵盖卧室、餐厅、医院、客厅与办公室五类典型室内场景。

搜集汇总

背景与挑战

背景概述

IDFire是一个专门用于室内火灾荷载识别的图像数据集，包含1015张标注了实例分割的室内场景图像，覆盖卧室、餐厅、医院、客厅和办公室五种典型环境。该数据集旨在支持自动火灾荷载估计，以替代传统耗时且易出错的人工调查方法，适用于计算机视觉和深度学习应用。