Disaster early warning method and system based on key target detection and image segmentation

By using YOLOv5 and U-Net algorithms in the disaster monitoring and early warning system for key target detection and image segmentation, and combining the LSTM model for multi-source data fusion, the problem of insufficient response speed and accuracy of the existing system is solved, real-time monitoring of disasters and high-precision early warning is achieved.

CN119649034AInactive Publication Date: 2025-03-18SICHUAN TOURISM UNIV
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Patent Information

Application Number
CN202510162426.5
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-02-14
Publication Date
2025-03-18
Estimated Expiration
Not applicable · inactive patent

AI Technical Summary

Technical Problem

The existing disaster monitoring and early warning systems have insufficient response speed and accuracy, making it difficult to deal with complex and changeable disaster scenarios, especially in the early stages of the disaster, and it is difficult to accurately predict the spread trend and threat range of disasters.

Method used

The disaster warning method based on key target detection and image segmentation is adopted. Through the YOLOv5 object detection algorithm and U-Net image segmentation algorithm, the key targets in the disaster scenario are quickly detected and accurately segmented. The LSTM model is combined for multi-source data fusion and time series analysis, and the dynamic prediction information and warning level of the disaster are output.

Benefits of technology

Real-time monitoring and early warning of disasters has been realized, which significantly improves the response speed and prediction accuracy of the early warning system, reduces false alarms and underreports, and provides more comprehensive disaster warning information.

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Abstract

The invention belongs to the field of disaster early warning, and provides a disaster early warning method and system based on key target detection and image segmentation, and the method comprises the steps: obtaining the real-time image data of a site, and carrying out the preprocessing of an image; processing the preprocessed image through a YOLOv5 target detection algorithm, detecting a key target in a disaster site, and outputting a category label and a bounding box of the target; performing pixel-by-pixel segmentation processing on a bounding box region output by target detection by using a U-Net image segmentation algorithm to generate an accurate segmentation mask of the target; and inputting the image segmentation mask, historical disaster data, meteorological data and geographic information system data into an LSTM prediction model, carrying out multi-source data fusion and time sequence analysis, and outputting dynamic prediction information and early warning levels of disasters. According to the invention, real-time monitoring and early warning of disasters are realized, the response speed of the early warning system is obviously improved, more comprehensive disaster early warning information is provided, high precision of disaster prediction is ensured, and the phenomena of false alarm and missing alarm are reduced.
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