This research presents an innovative real-time disaster detection framework that leverages YOLOv11, a deep learning model, to enhance situational awareness and decision-making in emergency response operations. Unlike traditional UAV-based systems that often suffer reduced accuracy in low-visibility or complex environments, the proposed approach fuses RGB and thermal imagery from quadcopter drones with the advanced feature extraction and high-speed inference capabilities of YOLOv11. Integrated into an edge computing platform, the system supports low-latency, real-time object detection, making it highly effective for time-critical disaster scenarios. To further support operational decision-making, a multi-criteria decision-making (MCDM) module based on the Analytic Hierarchy Process (AHP) is embedded within the pipeline, enabling automated prioritization of detected threats. The model was trained and validated on a 10,000-image multimodal dataset comprising annotated UAV data from wildfire, flood, and earthquake zones. YOLOv11 consistently outperformed baseline models such as YOLOv5, achieving 88% detection accuracy, with precision, recall, and F1-scores all exceeding 0.85, and reduced response time by 40% compared to manual inspection workflows. The integration of YOLOv11 with thermal-RGB fusion significantly improved detection robustness under smoke, haze, and debris-obscured conditions. This study validates YOLOv11 on multimodal UAV disaster imagery with an integrated decision-support layer to improve emergency response effectiveness. The proposed framework sets a new benchmark in intelligent aerial surveillance, combining high detection accuracy with real-time processing capabilities. Designed for cost-efficiency and modular deployment, the framework supports scalability across local governments, first responders, and humanitarian organizations.
This research presents an innovative real-time disaster detection framework that leverages YOLOv11, a deep learning model, to enhance situational awareness and decision-making in emergency response operations. Unlike traditional UAV-based systems that often suffer reduced accuracy in low-visibility or complex environments, the proposed approach fuses RGB and thermal imagery from quadcopter drones with the advanced feature extraction and high-speed inference capabilities of YOLOv11. Integrated into an edge computing platform, the system supports low-latency, real-time object detection, making it highly effective for time-critical disaster scenarios. To further support operational decision-making, a multi-criteria decision-making (MCDM) module based on the Analytic Hierarchy Process (AHP) is embedded within the pipeline, enabling automated prioritization of detected threats. The model was trained and validated on a 10,000-image multimodal dataset comprising annotated UAV data from wildfire, flood, and earthquake zones. YOLOv11 consistently outperformed baseline models such as YOLOv5, achieving 88% detection accuracy, with precision, recall, and F1-scores all exceeding 0.85, and reduced response time by 40% compared to manual inspection workflows. The integration of YOLOv11 with thermal-RGB fusion significantly improved detection robustness under smoke, haze, and debris-obscured conditions. This study validates YOLOv11 on multimodal UAV disaster imagery with an integrated decision-support layer to improve emergency response effectiveness. The proposed framework sets a new benchmark in intelligent aerial surveillance, combining high detection accuracy with real-time processing capabilities. Designed for cost-efficiency and modular deployment, the framework supports scalability across local governments, first responders, and humanitarian organizations.