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Article | Open Access

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

Bofei ZHAOHaigang SUI( )Yihao ZHUChang LIUWentao WANG
State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China
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Abstract

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.

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Journal of Geodesy and Geoinformation Science
Pages 74-89
Cite this article:
ZHAO B, SUI H, ZHU Y, et al. Real-time Rescue Target Detection Based on UAV Imagery for Flood Emergency Response. Journal of Geodesy and Geoinformation Science, 2024, 7(1): 74-89. https://doi.org/10.11947/j.JGGS.2024.0106

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Published: 20 March 2024
© 2024 Journal of Geodesy and Geoinformation Science
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