The application relates to the technical field of target detection, and discloses a multi-stage refined
camouflage target detection method based on
wavelet enhancement recognition, which makes up for the defects that the existing method cannot effectively utilize
global information, leading to missing of detection details, and cannot process multi-layer information, resulting in feature redundancy problems, so that the method can better complete a position target detection task. First, a visible light image is preprocessed through a pre-training model, a
pyramid operation is performed to expand the feature quantity, the amount of calculation is controlled while the amount of information is ensured, part of the initial features are selected for feature enhancement, and the features are screened and identified through a
wavelet identification enhancement module; the highest layer features are selected and sent into an asymmetric
convolution mamba module to obtain features with
global information; then, a feature reverse decoder composed of multiple guide units is used to process the
global information features and the identification features to obtain a prediction map, and the model is trained; when a
specific detection task is performed, the trained model is used to obtain the prediction map, and the detection task is completed.