Few-sample target detection method based on meta-feature and weight adjustment and network model

The invention discloses a few-sample target detection method based on meta-feature and weight adjustment and a network model. The method comprises the following steps: S1, constructing a detection network model and preprocessing an image; s2, extracting meta-features and weight vectors of the base class images; s3, combining the extracted meta-features and weight vectors to obtain a multi-dimensional feature map, and inputting the multi-dimensional feature map into a classification regression module to calculate a loss function; s4, adjusting network parameters according to the loss function and the gradient descent, and realizing training of a detection network model by the base class image; s5, extracting meta-features and weight vectors of the base class and new class joint images; s6,repeating the step S3 and the step S4, and training of the new class and base class combined image on the detection network model is completed; and S7, detecting the test image by using the trained detection network model. Ac