Research and Application of Image Recognition Technology for Disastrous Weather Events in Hebei
WEI Tiexin
SI Lili
ZHAO Liang
ZHANG Jing
SUN Bin
Abstract：To effectively utilize the extensive image data of disastrous weather events observed by the public,and to develop new monitoring methods,we created a training set using images of disastrous weather events shared by the public.Based on the ResNet-50 convolutional neural network,an image recognition model was developed for eight types of disastrous weather events.The model's accuracy and operational efficiency were enhanced through a secondary correction technique using gridded meteorological parameters,as well as an optimization mode involving online auditing and offline updates.After optimization,the average recognition accuracy rate of the eight models exceeded 80%.This technology has been applied to the multi-source meteorological disaster monitoring and documentation in Hebei Province,significantly increasing the number of images obtained for disastrous weather events compared to traditional methods.
Keywords：disastrous weatherimage recognitionobservation by the publicResNet-50 convolutional neural networkgridded meteorological correction
Publication Date：2024-12-28
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：10( 983-992 )