It is critical for fire-rescuers to rapidly identify flammable and explosive materials around a burning building and accurately locate trapped individuals within the golden rescue period. To address this need, this paper proposes and develops FireSeer—an automated system for fire detection and unit-level localization. Deploying aerial vehicles, FireSeer assists rescue teams by quickly identifying burning units and their floor levels. Technically, the system determines the specific location of fire-affected floors through a three-stage processing pipeline consisting of a Finder, a Detector, and a Localizer. The Finder extracts key information, such as fire signatures and building corners, from onboard camera images, and isolates the building foreground via perspective transformation. Subsequently, the Detector identifies individual units on fire. In the Localizer stage, a floor classification method categorizes the detected units and outputs the specific unit number where the fire is located. Additionally, a prediction and optimization module corrects missed detections, false positives, and occlusions. Experiments using both synthetic and real-world data confirm the algorithm’s real-time feasibility on embedded devices. Results from typical scenarios show that the FireSeer prototype achieves a row prediction accuracy (RA) of over 97% and a column prediction accuracy (CA) of over 86%, with an average processing time of 2.72 ms—demonstrating the system’s high precision and lightweight efficiency.
Zhai et al. (Tue,) studied this question.