The application discloses a deep-sea rare
biological target detection method based on a stable
diffusion model and belongs to the technical field of deep-sea
biological target detection. Rare biological categories are determined from a deep-sea benthic biological real scene
data set, and a foreground region image is extracted to construct a real target
data set, a foreground
image generation model is trained, and diversified synthetic foreground images are generated. A conditional
control network is used to generate an external drawing of the foreground image according to a
layout mask, the synthetic foreground image is fused with a deep-sea background, and a target position
label is automatically generated according to the
layout mask to obtain a synthetic scene image. After the synthetic quality is screened and the
label frame is refined, the synthetic scene image is combined with the real scene
data set to construct an enhanced training data set, and a target detection model is trained. The application solves the problem that the target
detection performance is limited due to the long-
tail distribution effect of the deep-sea exploration data set, and improves the detection capability of the target detection model for the rare biological categories.