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.