The application discloses a marine ship detection method based on
feature extraction and feature weighting selection fusion. First,
remote sensing ship images are collected to construct a
data set. Then, a ship detection model is constructed, the
remote sensing ship images are input into the model to obtain fusion features, and then the fusion features are input into a detection head of the model for detection to obtain a detection result. Then, the model is trained, the
remote sensing ship images are input into the model, and after multiple rounds of training, a final model is obtained. Finally, the remote sensing ship images are input into the trained model to output ship types and positioning information. The application utilizes
dynamic learning sampling point offset and modulation factors to enable
convolution kernels to change according to ship shapes and enhance feature information. In the
feature fusion stage, feature
pyramid networks based on hierarchical scales are developed to fully utilize feature maps from different scales and enhance the feature expression capability of the model.