The present application relates to the field of
signal detection, and particularly to a non-cooperative unmanned aerial vehicle
burst frequency point detection method and device based on a Seg-Yolo model. The Seg-Yolo model proposed by the present application does not require prior sequences of non-cooperative unmanned aerial vehicle signals at all, and does not require prediction of specific sequences, and can directly realize continuous, discontinuous, overlapping and non-overlapping
signal frequency point detection in a time-frequency graph. Through preprocessing,
image noise reduction and the Seg-Yolo model, the present application reduces the image volume that needs to be processed and simplifies the size of the
deep learning network, can accurately lock the
center frequency of each frequency point on the basis of reducing the
training time and improving the training efficiency, and improves the accuracy and reliability of
signal extraction based on complex frequency hopping signals.