The invention discloses a CML-sinGAN-based side-scan
sonar underwater target image sample amplification method, and belongs to the technical field of side-scan
sonar image target recognition and amplification and
deep learning. The method specifically comprises the steps that firstly, an eight-layer-scale
pyramid network structure is designed according to the characteristics of a side-scan
sonar image, and the diversity of a generated image is improved while image textures are fully learned; secondly, fusion is carried out in a generator, and information dispersion is reduced while target learning is enhanced; then, a
loss function based on L1loss is introduced into the
discriminator, the discrimination capability of the
discriminator on the authenticity of the target is enhanced, and unreal false pictures are removed; and finally, based on the conversion model, carrying out high-quality amplification on the existing
small sample side-scan sonar image, and carrying out YOLOv7 identification detection by using the amplified image. The method provides a new way for side-scan sonar
small target sample amplification and a high-performance
underwater target detection model.