The invention discloses an intelligent interference detection method based on time-frequency semantic perception, and belongs to the field of signal processing, and the basic idea for realizing the intelligent interference detection method is as follows: firstly, converting an original echo signal carrying interference into a time-frequency domain by using short-time Fourier transform, then establishing a U-net semantic segmentation network, and extracting image features by using a convolutional layer; restoring the size of an original image at a decoding part by using an up-sampling layer, so that labels are conveniently compared pixel by pixel to determine a threshold value; fusing low-layer information of a coding part and high-layer information of a decoding part by a splicing layer, and making up details and edge information lost along with network deepening, so that fine segmentation under a small sample is realized; on this basis, training the model, carrying out interference and background identification on other data sets, to verify the segmentation effect of the model. Segmentation thresholds are divided autonomously by means of deep learning, and interference and background segmentation and recognition are intelligently realized; meanwhile, due to the fact that the U-net network with a small model is adopted, the number of training sets needed for feature extraction is reduced, and the purpose that a good segmentation effect can be achieved on interference detection application only through few training sets is achieved.
Intelligent interference detection method based on time-frequency semantic perception