Real-time Rescue Target Detection Based on UAV Imagery for Flood Emergency Response

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Real-time Rescue Target Detection Based on UAV Imagery for Flood Emergency Response

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Bofei ZHAO, Haigang SUI, Yihao ZHU, Chang LIU, Wentao WANG

Timely acquisition of rescue target information is critical for emergency response after a flood disaster. Unmanned Aerial Vehicles (UAVs) equipped with remote sensing capabilities offer distinct advantages, including high-resolution imagery and exceptional mobility, making them well suited for monitoring flood extent and identifying rescue targets during floods. However, there are some challenges in interpreting rescue information in real time from flood images captured by UAVs, such as the complexity of the scenarios of UAV images, the lack of flood rescue target detection datasets and the limited real-time processing capabilities of the airborne on-board platform. Thus, we propose a real-time rescue target detection method for UAVs that is capable of efficiently delineating flood extent and identifying rescue targets (i.e., pedestrians and vehicles trapped by floods). The proposed method achieves real-time rescue information extraction for UAV platforms by lightweight processing and fusion of flood extent extraction model and target detection model. The flood inundation range is extracted by the proposed method in real time and detects targets such as people and vehicles to be rescued based on this layer. Our experimental results demonstrate that the Intersection over Union (IoU) for flood water extraction reaches an impressive 80%, and the IoU for real-time flood water extraction stands at a commendable 76.4%. The information on flood stricken targets extracted by this method in real time can be used for flood emergency rescue.