Fire Vigilance Pocket: An Intelligent APP for Real-Time Fire Hazard Quantification

Abstract

Real-time fire hazard estimation is an essential step for smart firefighting practice. This paper introduces the Fire Vigilance Pocket Edition application (FV Pocket), which is designed to enable automatic fire identification and quantification using computer vision and deep learning techniques, for real-time fire surveillance. The application comprises four main functions, namely, fire detection, fire segmentation, fire measurement, and fire calorimetry. Fire detection is performed by YOLOv5, which localizes the fire source in the image and marks the location of the flame area. Subsequently, the detected fire area is input into the Swin-Unet model to separate the flame and background, enabling the real-time display of the fire area. Additionally, image-based fire measurement techniques are used to determine the flame height and the flame area according to the estimated reference scales, which also facilitates the rescaling of raw images. Finally, the rescaled images are fed into a pre-trained fire calorimetry model to identify the heat release rate of the fire. The models used in FV Pocket, their design, and main features are discussed, and the application is demonstrated using real fire events under various scenarios. The potential uses and limitations of FV Pocket are also addressed in this work.