This invention discloses a low-altitude
wind shear recognition system and method based on convolutional neural networks, belonging to the field of meteorological monitoring technology. The
system includes: a low-altitude
wind shear dataset construction module, which generates a
wind shear image dataset containing
crosswind shear, low-altitude
jet stream, headwind and tailwind shear, and micro-downbursts; a data preprocessing module, which performs preprocessing on the images; a
convolutional neural network module, which incorporates a lightweight
convolutional neural network with a progressive channel design, trained using a two-stage transfer learning strategy, and utilizes an improved
loss function obtained by weighted summation of cross-entropy loss, flow direction consistency constraint loss, and global vortex intensity constraint loss to obtain a wind shear recognition model; and a wind shear intelligent classification module, used to identify the type of wind shear in low-altitude
wind field images in real time. This invention has the advantages of high recognition accuracy, low computational load, good real-time performance, and low hardware cost, and can effectively ensure the
flight safety of UAVs in complex low-altitude
wind field environments.