Few-shot sonar images target detection in complex backgrounds
Underwater target detection is a research problem of great significance and plays an important role in both military and civilian fields.The sonar images available in real scenes are very limited and the low signal-to-noise ratio of the sonar images does not allow for satisfactory detection results.Therefore,this paper introduces few-shot learning,based on the Faster R-CNN two-stage target detection algorithm,and chooses different strategies to optimize the model,obtaining bet-ter detection results and verifying the feasibility of few-shot target detection in the field of sonar images.Then,simulation experiments are conducted to obtain sonar images under different reverberation backgrounds according to the effect of rever-beration on sonar images,and the detection performance of the training model under different datasets is compared and ana-lyzed.Experimental results show that adding reverberation signals to the training samples can improve the target detection accuracy under low signal-to-noise ratio conditions.
万方数据
维普
