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

doi:10.11947/j.JGGS.2024.0106

Abstract

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.

Key words: UAV; flood extraction; target rescue detection; real time

ZHAO Bofei, SUI Haigang, ZHU Yihao, LIU Chang, WANG Wentao. Real-time Rescue Target Detection Based on UAV Imagery for Flood Emergency Response[J]. Journal of Geodesy and Geoinformation Science, 2024, 7(1): 74-89.

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References 27

[1]	IPCC. Managing the risks of extreme events and disasters to advance climate change adaptation[R]. Special Report of Working Groups I and II of the Intergovernmental Panel on Climate Change, Cambridge: Cambridge University Press, 2012.
[2]	CRED, UNDRR. The human cost of weather related disasters: 1995-2015[R]. Geneva: Centre for Research on the Epidemiology of Disasters (CRED), United Nations International Strategy for Disaster Reduction, 2015.
[3]	DOTTORI F, SZEWCZYK W, CISCAR J C, et al. Increased human and economic losses from river flooding with anthropogenic warming[J]. Nature Climate Change, 2018, 8(9):781-786. DOI: 10.1038/s41558-018-0257-z.
[4]	FANG Jian, ZHANG Chaoyang, FANG Jiayi, et al. Increasing exposure to floods in China revealed by nighttime light data and flood susceptibility mapping[J]. Environmental Research Letters, 2021, 16(10): 104044. DOI: 10.1088/1748-9326/ac263e.
[5]	KUANG Da, LIAO K H. Learning from floods: linking flood experience and flood resilience[J]. Journal of Environmental Management, 2020, 271: 111025. DOI: 10.1016/j.jenvman.2020.111025.
[6]	MARANZONI A, D'Oria M, RIZZO C. Quantitative flood hazard assessment methods: a review[J]. Journal of Flood Risk Management, 2023, 16(1): e12855. DOI: 10.1111/jfr3.12855.
[7]	ALBERTINI C, GIOIA A, IACOBELLIS V, et al. Detection of surface water and floods with multispectral satellites[J]. Remote Sensing, 2022, 14(23): 6005. DOI: 10.3390/rs14236005.
[8]	YAN Pu, FANG Yue, GHEN Jie, et al. Automated extraction for water bodies using new water index from Landsat 8 OLI images[J]. Journal of Geodesy and Geoinformation Science, 2023, 6(1): 59-75. DOI: 10.11947/j.JGGS.2023.0105.
[9]	MEMON A A, MUHAMMAD S, RAHMAN S, et al. Flood monitoring and damage assessment using water indices: a case study of Pakistan flood-2012[J]. The Egyptian Journal of Remote Sensing and Space Science, 2015, 28(1): 99-106. DOI: 10.1016/j.ejrs.2015.03.003.
[10]	ADAMS S M, FRIEDLAND C J. A survey of Unmanned Aerial Vehicle (UAV) usage for imagery collection in disaster research and management[C]// Proceedings of the 9th International Workshop on Remote Sensing for Disaster Response. Stanford, CA: [s.n.], 2011.
[11]	GONG Jianya, JI Shunping. Photogrammetry and deep learning[J]. Acta Geodaetica et Cartographica Sinica, 2018, 47(6): 693-704. DOI: 10.11947/j.AGCS.2018.20170640.
[12]	RAHNEMOONFAR M, CHOWDHURY T, SARKAR A, et al. FloodNet: a high resolution aerial imagery dataset for post flood scene understanding[J]. IEEE Access, 2021, 9: 89644-89654. DOI: 10.1109/ACCESS.2021.3090981.
[13]	MUNAWAR H S, ULLAH F, QAYYUM S, et al. Application of deep learning on UAV-based aerial images for flood detection[J]. Smart Cities, 2021, 4(3): 1220-1242. DOI: 10.3390/smartcities4030065.
[14]	SAFAVI F, CHOWDHURY T, RAHNEMOONFAR M. Comparative study between real-time and non-real-time segmentation models on flooding events[C]// Proceedings of 2021 IEEE International Conference on Big Data (Big Data). Orlando, FL: IEEE, 2021: 4199-4207.
[15]	HERNÁNDEZ D, CECILIA J M, CANO J C, et al. Flood detection using real-time image segmentation from unmanned aerial vehicles on edge-computing platform[J]. Remote Sensing, 2022, 14(1): 223. DOI: 10.3390/rs14010223.
[16]	INTHIZAMI N S, MA'SUM M A, ALHAMIDI M R, et al. Flood video segmentation on remotely sensed UAV using improved Efficient Neural Network[J]. ICT Express, 2022, 8(3): 347-351. DOI: 10.1016/j.icte.2022.01.016.
[17]	TAN Mingxing, LE Q V. EfficientNet: rethinking model scaling for convolutional neural networks[C]// Proceedings of the 36th International Conference on Machine Learning. Long Beach, CA: PMLR, 2019.
[18]	LIANG Yongqing, LI Xin, TSAI B, et al. V-FloodNet: a video segmentation system for urban flood detection and quantification[J]. Environmental Modelling & Software, 2023, 160: 105586. DOI: 10.1016/j.envsoft.2022.105586.
[19]	PI Yalong, NATH N D, BEHZADAN A H. Detection and semantic segmentation of disaster damage in UAV footage[J]. Journal of Computing in Civil Engineering, 2021, 35(2): 04020063. DOI: 10.1061/(ASCE)CP.1943-5487.0000947.
[20]	RONNEBERGER O, FISCHER P, BROX T. U-Net: convolutional networks for biomedical image segmentation[C]// Proceedings of the 18th International Conference Medical Image Computing and Computer-Assisted Intervention. Munich: Springer, 2015.
[21]	WOO S, PARK J, LEE J Y, et al. CBAM: convolutional block attention module[C]// Proceedings of the 15th European Conference on Computer Vision-ECCV 2018. Munich: Springer, 2018: 3-19.
[22]	ZHAO Hengshuang, SHI Jianping, QI Xiaojuan, et al. Pyramid scene parsing network[C]// Proceedings of 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Honolulu, HI: IEEE, 2017.
[23]	CHEN L C, ZHU Yukun, PAPANDREOU G, et al. Encoder-decoder with atrous separable convolution for semantic image segmentation[C]// Proceedings of the 15th European Conference on Computer Vision--ECCV 2018. Munich: Springer, 2018.
[24]	SANDLER M, HOWARD A, ZHU Menglong, et al. MobileNetV2: inverted residuals and linear bottlenecks[C]// Proceedings of 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Salt Lake City, UT: IEEE, 2018.
[25]	PRABHU B V B, LAKSHMI R, ANKITHA R, et al. RescueNet: YOLO-based object detection model for detection and counting of flood survivors[J]. Modeling Earth Systems and Environment, 2022, 8(4): 4509-4516. DOI: 10.1007/s40808-022-01414-6.
[26]	LIU Shihan, ZHA Junlin, SUN Jian, et al. EdgeYOLO: an edge-real-time object detector[C]// Proceedings of the 42nd Chinese Control Conference (CCC). Tianjin:IEEE, 2023.
[27]	LIANG Siyuan, WU Hao, ZHEN Li, et al. Edge YOLO: real-time intelligent object detection system based on edge-cloud cooperation in autonomous vehicles[J]. IEEE Transactions on Intelligent Transportation Systems, 2022, 23(12): 25345-25360. DOI: 10.1109/TITS.2022.3158253.