A Large Scale Dataset For Fire Detection and Segmentation in Indoor Spaces

Fire represents a dangerous event, especially in inhabited areas where it can cause extensive economical damage, as well as take human lives, and therefore early fire detection is of utmost importance and requires careful attention. Utilizing images from security cameras and computer vision algorithms, it is possible to detect and raise the alarm in the event of a fire. The existence of similar-colored lights to the flame's color is the greatest obstacle to indoor fire detection when it comes to computer vision. The lights may trigger false positive detections, resulting in false alarms and potential fire suppression by automated systems. By developing a new fire dataset for the training of deep neural networks, we attempted to circumvent the stated issue. Our dataset includes images of different colored lights, images with reflections of light that resemble the color of fire, and images of fire in a variety of environments, including warehouses, factories, shopping malls, residential buildings, offices, etc. Although there are numerous scientific papers and datasets for fire detection, there are not many datasets containing images of indoor fires. In this paper, we show a process of collecting and annotating images representing indoor fire in a manner suitable for deep neural network training. Furthermore, we present the developed Fire Sense image annotation tool and the process of image annotation. The dataset currently consists of more than 11000 annotated images of various types of fires in different environments.